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Bridging the AI Education, Knowledge, and Skills Gap of Library and Information Professionals: Evaluation of the Innovation, Inquiry, Disruption, and Access (IDEA) Institute on Artificial Intelligence

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Title: Bridging the AI Education, Knowledge, and Skills Gap of Library and Information Professionals: Evaluation of the Innovation, Inquiry, Disruption, and Access (IDEA) Institute on Artificial Intelligence
Language: English
Authors: Dania Bilal, Clara M. Chu, Soo Young Rieh, Yujin Choi
Source: Journal of Education for Library and Information Science. 2025 66(4):340-362.
Availability: Association for Library and Information Science Education. Available from: University of Toronto Press. 5201 Dufferin Street, Toronto, ON, M3H 5T8 Canada. Tel: 416-667–7929; Fax: 416-667–7832; e-mail: journals@utpress.utoronto.ca; e-mail: office@alise.org; Web site: https://www.utpjournals.press/loi/jelis
Peer Reviewed: Y
Page Count: 23
Publication Date: 2025
Sponsoring Agency: Institute of Museum and Library Services (IMLS)
Contract Number: RE246419OLS20
Document Type: Journal Articles
Reports - Research
Education Level: Adult Education
Descriptors: Artificial Intelligence, Professional Development, Continuing Education, Information Scientists, Program Effectiveness, Program Attitudes
DOI: 10.3138/jelis-2024-0033
ISSN: 0748-5786
2328-2967
Abstract: Artificial Intelligence (AI) is reshaping all sectors of society, including libraries. AI adoption in libraries has been gradual due to concerns and challenges, including ethical issues, maturity of the technology, insufficient AI education and training designed for library and information professionals, and gaps in AI education in library and information science (LIS) programs. This case study reports on the motivations, processes, and evaluations of the IDEA Institute on AI that was developed to equip two cohorts (Fellows) of information professionals who participated in the 2021 and 2022 IDEA Institute on AI with the foundational knowledge and skills to lead AI work. A multi-method approach was used to collect and analyze the evaluation data from multiple sources at different points of the IDEA Institute on AI. The IDEA Institute on AI applied an outcome-based planning and evaluation model and employed formative and summative evaluations using surveys and focus-group discussions. Fellows worked in various library and information environments, most in academic libraries. The case study results showed that the Fellows' AI knowledge and skills increased substantially, their confidence greatly increased upon completing the IDEA Institute on AI, and they engaged in AI projects in their workplaces. They built awareness of AI issues and challenges and developed a comprehensive understanding of AI within the context of equity, diversity, inclusion, and accessibility. The Fellows' supervisors were positive about the learning and experience their Fellows gained from the IDEA Institute on AI and their peers. The results of this case study have significant implications for developing AI professional development programs in the LIS field, providing exemplary AI education and training as AI technology evolves, including generative AI and large language models, and integrating AI into LIS curricula.
Abstractor: As Provided
Entry Date: 2025
Accession Number: EJ1485426
Database: ERIC
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  Value: <anid>AN0188368706;lii01oct.25;2025Oct03.06:49;v2.2.500</anid> <title id="AN0188368706-1">Bridging the AI Education, Knowledge, and Skills Gap of Library and Information Professionals: Evaluation of the Innovation, Inquiry, Disruption, and Access (IDEA) Institute on Artificial Intelligence </title> <p>Artificial Intelligence (AI) is reshaping all sectors of society, including libraries. AI adoption in libraries has been gradual due to concerns and challenges, including ethical issues, maturity of the technology, insufficient AI education and training designed for library and information professionals, and gaps in AI education in library and information science (LIS) programs. This case study reports on the motivations, processes, and evaluations of the IDEA Institute on AI that was developed to equip two cohorts (Fellows) of information professionals who participated in the 2021 and 2022 IDEA Institute on AI with the foundational knowledge and skills to lead AI work. A multi-method approach was used to collect and analyze the evaluation data from multiple sources at different points of the IDEA Institute on AI. The IDEA Institute on AI applied an outcome-based planning and evaluation model and employed formative and summative evaluations using surveys and focus-group discussions. Fellows worked in various library and information environments, most in academic libraries. The case study results showed that the Fellows' AI knowledge and skills increased substantially, their confidence greatly increased upon completing the IDEA Institute on AI, and they engaged in AI projects in their workplaces. They built awareness of AI issues and challenges and developed a comprehensive understanding of AI within the context of equity, diversity, inclusion, and accessibility. The Fellows' supervisors were positive about the learning and experience their Fellows gained from the IDEA Institute on AI and their peers. The results of this case study have significant implications for developing AI professional development programs in the LIS field, providing exemplary AI education and training as AI technology evolves, including generative AI and large language models, and integrating AI into LIS curricula.</p> <p>Keywords: Artificial Intelligence (AI); education; evaluation; IDEA Institute on Artificial Intelligence; library and information professionals</p> <hd id="AN0188368706-2">Key Points:</hd> <p></p> <ulist> <item> AI adoption in libraries has been gradual due to concerns and challenges, including ethical issues, maturity of the technology, insufficient AI education and training designed for library and information professionals, cost, and gaps in AI education in library and information science (LIS) programs.</item> <p></p> <item> Library and information professionals must keep abreast of technological advances and take the lead in providing innovations in AI applications in the workplace to provide innovative and value-added services to meet their users' evolving information needs and expectations. Additionally, they should be capable of mitigating AI challenges, including concerns about inequality, discrimination, data privacy, safety, ethics, and bias.</item> <p></p> <item> The IDEA Institute on AI filled an AI education gap for library and information professionals. It equipped two cohorts of information professionals from various types of libraries with the foundational knowledge and skills to lead AI projects. The cohorts developed a comprehensive understanding of AI within the context of equity, diversity, inclusion, and accessibility and gained knowledge of the conceptual, technical, social, and ethical aspects of AI.</item> </ulist> <p>Artificial Intelligence (AI) is a fast-growing field driving innovation and reshaping all sectors of society, including libraries. AI has the potential to revolutionize library services and operations in many ways, including enhancing user information discovery, providing personalized recommendations, answering user questions, optimizing collection management, and improving access to library collections (e.g., [<reflink idref="bib4" id="ref1">4</reflink>]; [<reflink idref="bib6" id="ref2">6</reflink>]; [<reflink idref="bib14" id="ref3">14</reflink>]; [<reflink idref="bib22" id="ref4">22</reflink>]). Library and information professionals must keep abreast of technological advances and take the lead in providing innovations in AI applications in the workplace to provide innovative and value-added services to meet their users' evolving information needs and expectations. Additionally, they should be capable of mitigating AI challenges, including concerns about inequality, discrimination, data privacy, ethics, and bias (e.g., [<reflink idref="bib3" id="ref5">3</reflink>]; [<reflink idref="bib21" id="ref6">21</reflink>]; [<reflink idref="bib24" id="ref7">24</reflink>]).</p> <p>Initiating and managing AI projects for library services and operations requires education and training in using AI tools and solutions. Although AI applications in libraries are on the rise, [<reflink idref="bib15" id="ref8">15</reflink>] survey found that only 8% (13/163) of respondents agreed that their research library in the United States or Canada was actively using AI in some capacity. Fewer libraries have offered AI training that empowers librarians with knowledge and skills for leading AI initiatives. [<reflink idref="bib13" id="ref9">13</reflink>] echoed the shortage of skills and awareness of AI in academic library environments. While many LIS professional organizations, such as the [<reflink idref="bib16" id="ref10">16</reflink>], the American Library Association (ALA), and the Association for College and Research Libraries (ACRL), have offered webinars and organized conference sessions about AI in libraries, these tend to be cursory, falling short of combining the conceptual, technical, social, and ethical aspects of AI.</p> <p>A gap exists in AI education and training because library and information science (LIS) education programs, including the iSchools in North America, have lagged in integrating AI topics into their curriculum ([<reflink idref="bib2" id="ref11">2</reflink>]; [<reflink idref="bib23" id="ref12">23</reflink>]). [<reflink idref="bib25" id="ref13">25</reflink>] reviewed the curriculum of five LIS programs in Australia. They identified several opportunities for preparing librarians, including conducting thorough evaluations of potential AI/robotics solutions in the library profession, increasing research on how AI and robotics influence information behavior, and offering continuing professional development and micro-credentials to target the necessary skills for practitioners. Thus, offering intensive AI education and training customized to the needs of library and information professionals is critical to filling the education gap and empowering library and information professionals to use and implement AI effectively and responsibly in libraries.</p> <p>The IDEA (Innovation, Disruption, Enquiry, Access) Institute on Artificial Intelligence (AI) (<ulink href="http://idea.infosci.utk.edu">http://idea.infosci.utk.edu</ulink>) was designed to address the education and training gap, with the funding provided by the Institute of Museum and Library Services (IMLS) Laura Bush 21st Century Librarian program. The Institute was offered in 2021 and 2022 as a one-week intensive, interactive, evidence-based, and applications-oriented professional development program for library and information professionals. Its purpose was to build and enhance the knowledge and skills of current and future library and information professionals in AI in a collaborative learning environment. It also aimed at building a collective and sustainable community of AI leaders by recruiting two cohorts of participants (known as Fellows) who shared their work experience and professional practice to design, develop, evaluate, and implement AI solutions in their workplaces.</p> <p>This paper is a case study that presents the motivations, processes, and evaluation of the IDEA Institute on AI as an example of a professional development program designed to fill a gap in AI education and training in the library and information professions. The findings from the case study have implications for designing future professional development programs for library and information professionals. This program was developed with a team of instructors with expertise in AI in diverse library and information backgrounds. The case study offers insights into its implementation to guide future efforts in developing AI education and training in the LIS field.</p> <p>To fill the AI education and training gap, we addressed the following research questions:</p> <p>RQ1: To what extent did the program advance the knowledge and skills of library and information professionals (i.e., the IDEA Institute on AI Fellows)?</p> <p>RQ2: How do the Fellows perceive their learning of AI from the program?</p> <p>RQ3: How do the workplace supervisors perceive the Fellows' learning of AI from the program?</p> <p>Specifically, this case study reports the findings of the Fellows' learning outcomes, acceptance of AI as a transformative technology, and adoption of AI applications in their workplaces. In addition, it reports the supervisors' perceptions of the Fellows' AI knowledge and skills in using and applying AI in the workplace. It includes the challenges the research PIs faced and the lessons they learned in offering the Institute. The introduction of the IDEA Institute on AI and the results from the program evaluation provide insights for LIS educators and trainers on how they can collaborate to develop future professional development programs that will bridge the AI education and training gap of library and information professionals. In addition, the AI curriculum developed for the 2021 and 2022 IDEA Institute on AI can be used as a model for teaching and understanding AI from conceptual, technical, ethical, and social perspectives and for learning and adopting AI in various library and information environments.</p> <hd id="AN0188368706-3">Literature review</hd> <p></p> <hd id="AN0188368706-4">AI competencies for library and information professionals</hd> <p>In recent years, a number of authors (e.g., [<reflink idref="bib8" id="ref14">8</reflink>]; [<reflink idref="bib9" id="ref15">9</reflink>]; [<reflink idref="bib10" id="ref16">10</reflink>]; [<reflink idref="bib12" id="ref17">12</reflink>]; [<reflink idref="bib15" id="ref18">15</reflink>]; [<reflink idref="bib27" id="ref19">27</reflink>]) have addressed the knowledge, skills, and competencies needed for library and information professionals tasked with leading AI projects in library settings. The recommended competencies ranged from understanding AI to ensuring that AI applications are ethical and aligned with professional values ([<reflink idref="bib7" id="ref20">7</reflink>]).</p> <p>OCLC's position paper "Responsible Operations: Data Science, Machine Learning, and AI in Libraries" ([<reflink idref="bib22" id="ref21">22</reflink>]) highlighted the challenges concerning workforce development, recommended core competencies, and evidence-based training and emphasized the importance of investing in developing internal talent rather than hiring new professionals, and providing educational and experiential learning opportunities to foster the growth of existing library and information professionals.</p> <p>[<reflink idref="bib11" id="ref22">11</reflink>] noted that librarians' information-mediating roles will shift toward a better understanding of data structures and translating existing skills in finding, managing, and preserving information into data. Specifically, they proposed the skills and knowledge for a variety of AI use cases, including but not limited to general knowledge of data and AI, computational methods, building a knowledge base of AI, project management, and the potential impact of AI on libraries and its implications for equality, diversity, and inclusion within the profession.</p> <p>[<reflink idref="bib13" id="ref23">13</reflink>] specified three essential analytical skills in AI that librarians need to acquire to effectively utilize the vast data available in libraries: predictive analytics, user behavior analytics, and learning analytics. While it is essential to identify new skills that librarians and information professionals need to utilize AI in library settings, [<reflink idref="bib7" id="ref24">7</reflink>] emphasized that "the strength of information professionals in taking up the opportunities created by AI is that they align to roles they already play" (p. 43).</p> <hd id="AN0188368706-5">Professional development programs, resources, and projects</hd> <p>Studies of librarians' perceptions of adopting AI in library settings ([<reflink idref="bib15" id="ref25">15</reflink>]; [<reflink idref="bib27" id="ref26">27</reflink>]) have suggested a growing need for training librarians on AI technologies. Hervieux and Wheatley's (2021) online survey of librarians in Canada and the United States showed that only 8% of the respondents (13/163) believed their library was actively using AI, 22% interacted with AI technology as part of their job duties, and 36% believed it would be possible for AI to replace some aspects of their job duties. [<reflink idref="bib27" id="ref27">27</reflink>] survey of public and academic librarians in North America found that 68% reported being interested in AI training. However, only 45% mentioned they could attain sufficient knowledge to adopt and utilize AI and related technologies in their library. [<reflink idref="bib20" id="ref28">20</reflink>] conducted a survey on AI literacy among academic library employees in the United States. and found that approximately 45% of respondents reported a moderate level of understanding of AI concepts and principles, while only 4% indicated a very high level of understanding. In terms of familiarity with AI tools, 31% reported a moderate level of experience. The study also revealed differences in AI understanding based on the area of academic librarianship: those in administration and management, as well as library instruction and information literacy, rated their understanding higher than other groups.</p> <p>Ever since the fall 2017 membership meeting of the Coalition for Networked Information (CNI) featured "Artificial Intelligence (AI) in libraries," international conferences, workshops, and webinars have emerged, showcasing presentations centered on discussing the potential of AI technologies in library settings. Topics ranged from ethics, education, governance, and policy to machine learning, data discovery and reuse, and robotics. AI4LAM, "an international, participatory community focused on advancing the use of artificial intelligence in, for and by libraries, archives and museums" (https://sites.google.com/view/ai4lam), has organized Fantastic Futures conferences since 2018. The Association for Information Science and Technology (ASIS&T) Special Interest Group on AI (SIG AI), founded by Soo Young Rieh, Dania Bilal, and Clara M. Chu, has organized an annual workshop as part of the ASIS&T annual meeting since 2021.</p> <p>In recent years, there has been a growing number of training opportunities for library and information professionals. IFLA organized the "New Horizons in Artificial Intelligence in Libraries" in 2022 as a Satellite meeting, and ACRL offered a webinar titled "Artificial Intelligence (AI) in Academic Libraries: How New AI Services Can Enhance Library User Support." In August 2023, IFLA's online session on "Generative AI for Library and Information Professionals: North American Voices in Developing an IFLA Resource" explored the opportunities, challenges, and impacts of generative AI in the library and information profession and their ramifications in the broader information environment. In September 2023, the iFederation, the joint community of the Association for Library and Information Science Education (ALISE), ASIS&T, and the iSchools Consortium, organized the online session "AI Impact on Teaching & Learning."</p> <p>In addition to these professional development programs, LIS professional associations have issued statements on AI ethics and the implications of AI technologies. For example, the [<reflink idref="bib16" id="ref29">16</reflink>] released the Statement on Libraries and Artificial Intelligence in 2020, highlighting how to integrate AI and machine learning technologies into everyday work. The IFLA Statement also emphasizes that libraries can play a crucial role in lifelong learning by educating users on how to thrive in a society that extensively uses AI. The Association for Information Science and Technology (ASIS&T), [<reflink idref="bib1" id="ref30">1</reflink>] issued the "Statement on AI Ethics and the Contributions of Diverse Voices in the Discussion," in which they jointly emphasized the importance of identifying potential flaws and biases in AI algorithms and encouraged embracing the diversity of voices contributing to AI-related research in the information science and technology field. Since 2023, IFLA's AI Special Interest Group has compiled a resource list on generative AI for library and information professionals (https://<ulink href="http://www.ifla.org/generative-ai/),">www.ifla.org/generative-ai/),</ulink> which is regularly updated.</p> <p>Further, recent projects have commenced to address AI adoption in libraries. Ithaka S+R has initiated the Making AI Generative for Higher Education Project with 19 university partners to assess the AI applications that may impact teaching, learning, and research and explore the long-term needs of institutions, instructors, and scholars (https://sr.ithaka.org/blog/making-ai-generative-for-higher-education-2/). Another initiative is the Collaborative IDEA Institute on AI for Rural Communities & Librarianship (CIRCL), a joint project of 14 state libraries and the Gigabit Libraries Network, which aims to bring AI into rural communities ([<reflink idref="bib5" id="ref31">5</reflink>]).</p> <p>The growing number of professional development programs, resources, and projects over the past seven years is evident. However, most of these programs focus on academic and research libraries and the impact of AI applications in higher education and scholarly communities. The handful of professional development programs identified for library and information professionals often run for a short duration, ranging from an hour to a couple of days, providing insufficient knowledge and skills for library and information professionals to fully grasp AI from the conceptual, technical, applied, social, and ethical perspectives to enable them to adopt AI in the workplace competently.</p> <p>[<reflink idref="bib3" id="ref32">3</reflink>] addressed the AI education and training gap of library and information professionals, noting that while AI can be a transformative technology, it has challenges, including algorithmic bias, privacy, safety, ethical and social implications, and cost. Mitigating these challenges requires library and information professionals to acquire knowledge, skills, and understanding of AI and its applications offered by the IDEA Institute on AI.</p> <hd id="AN0188368706-6">The IDEA Institute on AI</hd> <p>The IDEA Institute on AI aimed to build and enhance current and future library and information professionals' knowledge and skills in AI by facilitating conceptual discussions and practical experiences in a collaborative learning environment. It also aimed to create a collective community of AI leaders where the participating library and information professionals would bring and share their work experience, expertise, and professional practice to design, develop, evaluate, and implement AI solutions in their workplace.</p> <p>The IDEA Institute on AI had three goals:</p> <p></p> <ulist> <item> to develop an innovative, forward-looking continuing education program on AI in library and information environments;</item> <p></p> <item> to develop leaders of AI in library and information environments, who, individually and as a collective, can innovate and create better awareness of AI as a "transformative" technology; and</item> <p></p> <item> to contribute an AI curriculum to fill a gap in AI education in LIS programs and library and information environments.</item> </ulist> <p>These goals were achieved through developing and offering the sustainable plan of the IDEA Institute on AI as a one-week intensive, interactive, evidence-based, and applications-oriented professional development program for library and information professionals. The IDEA Institute on AI's commitment to equity, diversity, and inclusion (EDI) occurred in all aspects of the program to deepen the knowledge and skills of library and information professionals in various types of libraries in using AI to enhance information access, discovery, and services for users, independent of race, ethnicity, gender identity, sexual orientation, socio-economic status, age, physical abilities, and other dimensions of potential inequity. The teaching of developing and applying AI technology in library and information environments is focused on human-centered design and mitigating data and algorithmic bias to ensure equity and fairness. The IDEA Institute on AI Project website is available at <ulink href="http://idea.infosci.utk.edu">http://idea.infosci.utk.edu</ulink>.</p> <hd id="AN0188368706-7">The team, instructors, and partners</hd> <p>The IDEA Institute on AI team consisted of three principal investigators (PIs), professors from different academic institutions, and four advisory board members who were also instructors. The team consisted of academics and practitioners with library and information expertise in various aspects of AI. A graduate assistant supported the team, one in each of the years of implementation from the respective host institutions: the University of Tennessee School of Information Sciences and the University of Texas at Austin School of Information.</p> <p>The project partnered with two associations. ASIS&T promoted the IDEA Institute on AI, provided workshop opportunities at the 2021 and 2022 ASIS&T Annual Meetings, and is sustaining the IDEA Institute on AI, including offering the IDEA Institute on AI beyond 2022. The ALA's Core: Leadership, Infrastructure, Futures promoted the call for participants, provided opportunities for presentations and programs at the ALA conferences and its workshops, and disseminated the IDEA Institute on AI's reports and resources via ALA's communication channels.</p> <hd id="AN0188368706-8">The cohorts</hd> <p>The IDEA Institute on AI was hosted in its first year at the University of Tennessee School of Information Sciences from July 11–16, 2021, and in its second year at the University of Texas at Austin School of Information from July 10–15, 2022. The IDEA Institute on AI was promoted in collaboration with the partners to a range of library and information lists and communities, intentionally targeting channels (e.g., ethnic library and information association lists, diversity-interest groups within associations) to recruit diverse applicants in alignment with the IDEA Institute on AI's EDI values.</p> <p>The IDEA Institute on AI had two cohorts of Fellows (i.e., the participants) who were selected from and represented diverse backgrounds, including education, experience, area of expertise, stage of career, work environments, US residency or citizenship, and full-time employment, with a balance in gender, age, and race/ethnicity to the extent possible, based on specific preset selection criteria. For each IDEA Institute on AI, the grant supported the travel and program cost of up to 15 Fellows, and an additional five Fellows were selected from applicants interested in attending the IDEA Institute on AI with their own funds.</p> <p>In 2021, 15 funded Fellows and two self-funded ones participated in the first year of the IDEA Institute on AI. In 2022, 14 funded and five self-funded Fellows participated in the second year of the IDEA Institute on AI. While the Fellows worked in a range of library and information environments, academic libraries predominated, with a few from public libraries (two in 2021 and one in 2022), one each year from LIS education, one from a school library in 2021, and one from a community college library in 2022 (Table 1). Academic libraries are taking the initiative to implement AI, wanting their existing staff to become knowledgeable about using AI and hiring specialized staff with a computer science background but with a limited library background to balance the technological knowledge and skills gap. The IDEA Institute on AI brought them and others interested in AI from other types of library environments to learn together. Although it may appear that a larger number of participants from academic libraries would bias the learning and focus of AI issues and applications, this was not the case. The cross-sector learning environment allowed everyone to contribute to each other's learning, identify common challenges and needs regarding AI in their respective library environments, and discuss concerns, directions, and solutions.</p> <p>Table 1: Sociodemographic characteristics of the cohorts</p> <p> <ephtml> <table><colgroup span="1"><col align="left" span="1" /><col align="left" span="1" /><col align="left" span="1" /></colgroup><thead><tr><th align="left" rowspan="1" colspan="1" /><th align="center" rowspan="1" colspan="1">2021 (<italic>n</italic> = 17)</th><th align="center" rowspan="1" colspan="1">2022 (<italic>n</italic> = 19)</th></tr></thead><tbody><tr><td colspan="3" align="left" rowspan="1">Background</td></tr><tr><td align="left" rowspan="1" colspan="1"> Academic Library</td><td align="center" rowspan="1" colspan="1">13</td><td align="center" rowspan="1" colspan="1">16</td></tr><tr><td align="left" rowspan="1" colspan="1"> Public Library</td><td align="center" rowspan="1" colspan="1">2</td><td align="center" rowspan="1" colspan="1">1</td></tr><tr><td align="left" rowspan="1" colspan="1"> School Library</td><td align="center" rowspan="1" colspan="1">1</td><td align="center" rowspan="1" colspan="1">0</td></tr><tr><td align="left" rowspan="1" colspan="1"> Community college</td><td align="center" rowspan="1" colspan="1">0</td><td align="center" rowspan="1" colspan="1">1</td></tr><tr><td align="left" rowspan="1" colspan="1"> LIS education</td><td align="center" rowspan="1" colspan="1">1</td><td align="center" rowspan="1" colspan="1">1</td></tr><tr><td colspan="3" align="left" rowspan="1">Gender</td></tr><tr><td align="left" rowspan="1" colspan="1"> Female</td><td align="center" rowspan="1" colspan="1">10</td><td align="center" rowspan="1" colspan="1">13</td></tr><tr><td align="left" rowspan="1" colspan="1"> Male</td><td align="center" rowspan="1" colspan="1">7</td><td align="center" rowspan="1" colspan="1">6</td></tr><tr><td colspan="3" align="left" rowspan="1">Ethnicity/Race</td></tr><tr><td align="left" rowspan="1" colspan="1"> African American</td><td align="center" rowspan="1" colspan="1">3</td><td align="center" rowspan="1" colspan="1">1</td></tr><tr><td align="left" rowspan="1" colspan="1"> Asian American</td><td align="center" rowspan="1" colspan="1">6</td><td align="center" rowspan="1" colspan="1">2</td></tr><tr><td align="left" rowspan="1" colspan="1"> Latina</td><td align="center" rowspan="1" colspan="1">1</td><td align="center" rowspan="1" colspan="1">2</td></tr><tr><td align="left" rowspan="1" colspan="1"> Middle Eastern</td><td align="center" rowspan="1" colspan="1">1</td><td align="center" rowspan="1" colspan="1">2</td></tr><tr><td align="left" rowspan="1" colspan="1"> White</td><td align="center" rowspan="1" colspan="1">6</td><td align="center" rowspan="1" colspan="1">12</td></tr></tbody></table> </ephtml> </p> <p>The data reported for gender and ethnicity/race were not explicitly requested in the application; rather, diversity information was obtained from various sources. Applicants volunteered information in their Personal Statements, the use of pronouns in reference letters to refer to them, the Fellows' use of pronouns with their names on Zoom in the pre-IDEA Institute on AI Program, and their gender self-references during the IDEA Institute on AI.</p> <p>Each cohort participated in mentoring activities throughout the program and became part of a professional network/community with an online discussion list, facilitating communication and resource sharing.</p> <hd id="AN0188368706-9">The program</hd> <p>The IDEA Institute on AI team co-developed the curriculum and co-designed the different elements of the IDEA Institute on AI: the pre-IDEA Institute on AI onboarding program, the one-week IDEA Institute on AI program, and post-IDEA Institute on AI activities. The pre-IDEA Institute on AI onboarding program occurred online two to three months before the IDEA Institute on AI. Using shared documents, the PIs—Bilal, Chu, and Rieh—facilitated the development of a learning community through online discussion of AI issues based on readings on AI and Machine Learning (ML) and the introduction of AI/ML, coding, and UX design by the IDEA Institute on AI team.</p> <p>The week-long IDEA Institute on AI consisted of lectures, hands-on exercises, lunch-and-learn sessions, demos, discussions of AI applications, reflection on learning experiences, and a capstone showcase open to the local library and information professionals and stakeholders. Five or more local AI experts were invited to a networking session over dinner to connect with the Fellows. One AI expert in the library field presented the latest AI applications online.</p> <hd id="AN0188368706-10">The curriculum</hd> <p>Five learning outcomes guided the curriculum: (<reflink idref="bib1" id="ref33">1</reflink>) to develop an understanding of AI technology as "disruptive" and "transformative" for augmenting and delivering services and improving workflows; (<reflink idref="bib2" id="ref34">2</reflink>) to lead innovation through adopting AI applications in library practice using the UX Methodology/Lifecyle as the conceptual framework; (<reflink idref="bib3" id="ref35">3</reflink>) to evaluate AI products; (<reflink idref="bib4" id="ref36">4</reflink>) to develop AI applications in their library and information environments; and (<reflink idref="bib5" id="ref37">5</reflink>) to transfer and broadly disseminate knowledge of AI in the workplace. The entire program, including the curriculum, is available at https://idea.infosci.utk.edu/pubs-talks. Table 2 shows selected curriculum topics from various AI perspectives.</p> <p>Table 2: Selected IDEA Institute on AI curriculum topics</p> <p> <ephtml> <table><colgroup span="1"><col align="left" span="1" /><col align="left" span="1" /></colgroup><thead><tr><th align="left" rowspan="1" colspan="1">Aspects</th><th align="left" rowspan="1" colspan="1">Topics</th></tr></thead><tbody><tr><td align="left" rowspan="1" colspan="1">Conceptual</td><td align="left" rowspan="1" colspan="1">AI challenges and opportunitiesUser experience (UX) design processes for AI</td></tr><tr><td align="left" rowspan="1" colspan="1">Social</td><td align="left" rowspan="1" colspan="1">Ethical considerations and guidelinesAI impact and valuesAlgorithmic bias</td></tr><tr><td align="left" rowspan="1" colspan="1">Applied</td><td align="left" rowspan="1" colspan="1">Survey of existing library, archive, and museum projectsAI project planning (project design, data collection, classification, and transformation; roles and implementation)Conversational AI – Theoretical foundationsConversational AI – Applications</td></tr><tr><td align="left" rowspan="1" colspan="1">Technical</td><td align="left" rowspan="1" colspan="1">Python basicsPython for machine learningAPIs and bibliometricsAI in information search and discoveryMachine learning and codingHarvesting, evaluating, and training data sets for use in AILinked open data machine learning for text with topic modeling and clustering</td></tr></tbody></table> </ephtml> </p> <hd id="AN0188368706-11">Program evaluation method</hd> <p>A multi-method approach was used to collect and analyze the evaluation data from multiple sources at multiple points of the IDEA Institute on AI. Figure 1 shows that data collection occurred at the start of the pre–IDEA Institute on AI onboarding program, during and at the end of the week-long IDEA Institute on AI, and six months after the IDEA Institute on AI. The IDEA Institute on AI applied an outcome-based planning and evaluation model and conducted two types of evaluation—formative and summative—to understand the project's progress and outcomes. The Fellows shared their interests and had a voice in their learning experience, which was incorporated into the program design. The program design allowed for external input, corroboration, and consensus building, with input from the advisory board members/instructors, corroboration from the Fellows' supervisors and observational showcase attendees, and consensus building from process observation at each team meeting and by Fellows during the IDEA Institute on AI. The Fellows' learning outcomes were measured in terms of perceptions of AI knowledge and skills in library and information environments. The advisory board members/instructors were involved in the project design and provided input in co-designing the curriculum and addressing the Fellows' interests (Figure 1).</p> <p>Graph: Figure 1: Program evaluation process and methods</p> <p>The pre–IDEA Institute on AI survey collected data on the Fellows' knowledge and competence at the start or before the program, and the same set of questions was used in the survey at the end of the IDEA Institute on AI to learn about their perceived skills and knowledge. This gauged the extent of learning that took place. On the third day of the IDEA Institute on AI, lunch-and-learn small-group conversations facilitated by the IDEA Institute on AI team served to obtain input from the Fellows on what was going well and what could be improved. As appropriate, changes were made in the second half of the IDEA Institute on AI.</p> <p>Fellows and supervisors filled out six-month post–IDEA Institute on AI surveys. From Fellows, the PIs learned about their application of an AI solution in the workplace. From supervisors, the PIs learned of their perspectives or observations of the Fellows' AI applications and sharing of AI knowledge in the workplace. After participating in the IDEA Institute on AI, some Fellows presented their AI projects at the 2021 and 2022 ASIS&T SIG AI workshops and the 2022 ALA annual conference and/or gave invited talks at national and international events.</p> <hd id="AN0188368706-12">Results</hd> <p>The formative and summative results of the IDEA Institute on AI program evaluation are presented in the context of the research question on advancing the knowledge and skills of library and information professionals and the two questions concerning the Fellows' perceptions of learning AI from the program and the supervisors' perceptions of their Fellows' learning of AI from the program.</p> <hd id="AN0188368706-13">Formative evaluation</hd> <p>During the 2021 and 2022 IDEA Institutes on AI, the PIs conducted formative evaluations of the IDEA Institute on AI over lunch-and-learn sessions. They elicited feedback from the Fellows about their AI learning experiences and the IDEA Institute on AI's program to identify areas for potential improvement and intervention. The first three authors facilitated discussions with the Fellows in small groups.</p> <p>Most 2021 Fellows appreciated the pre–IDEA Institute on AI onboarding program, especially the combination of conceptual and practical topic discussions, speaker presentations, breakout activities, and instructors' access. They mentioned that the onboarding program helped them understand how things were evolving and broadened their perspective on AI. There were multiple instances of the word "real" being used positively. The Fellows valued the interactive sessions, such as face-to-face interactions, human connections, hands-on learning, and discovering what other Fellows were doing and their varied backgrounds. Most stated that they found learning about other Fellows' work during the onboarding program and at the IDEA Institute on AI valuable. Many Fellows agreed that their experiences exceeded their initial expectations of the IDEA Institute on AI, saying "Super" and "A+++." They appreciated collaborating on a group project instead of working individually.</p> <p>Despite the largely positive feedback, the first cohort voiced some issues. A group of the 2021 Fellows agreed that varying levels of coding knowledge among the Fellows was the most significant barrier they experienced. They recommended assessing Fellows' limitations upfront, providing extra onboarding activities for those in need, forming teams to complement each other's knowledge gaps, and ensuring thorough review and contextualization of the material presented before progressing. The PIs incorporated the Fellows' suggestions into the 2022 IDEA Institute on AI. In the 2021 IDEA Institute on AI, they restructured the remaining coding exercise by relating it to a hypothetical library application and creating work groups of mixed coding ability so they could help each other. The second issue was the lack of time during the day to reflect, pause, or connect among the Fellows. Since the PIs had a lunchtime presentation on the fourth day, they could not address this issue in 2021. However, in 2022, the lunch periods were activity-free, except for the one lunch-and-learn session (i.e., formative evaluation small-group discussions).</p> <p>The 2022 Fellows who participated in the formative evaluation of the IDEA Institute on AI highlighted valuable experiences during the onboarding program and the IDEA Institute on AI. They appreciated the various activities, such as finding value in real-world examples and learning to work with different groups using AI and ML. In addition, they valued exploring the complexities of EDIA, AI bias, and their impact. They commented that providing structure, organization, and a program booklet during the onboarding program helped set clear expectations, ensuring transparency of the IDEA Institute on AI's objectives. They noted that the team activities provided a gradual approach to AI applications, making the learning process less overwhelming. In addition, they appreciated the guided questions and hands-on experience with Python, facilitating effective learning, even for those unfamiliar with coding. They noted that their initial expectations matched their experiences. Key factors contributing to this alignment were engaging in diverse activities and providing a supportive and non-pressured learning environment.</p> <hd id="AN0188368706-14">Summative evaluation</hd> <p>The summative evaluation covers the Fellows' pre– and post–IDEA Institute on AI surveys, six-month post-IDEA Institute on AI surveys, and the Fellows' supervisors' six-month post–IDEA Institute on AI survey.</p> <hd id="AN0188368706-15">Fellows' learning outcomes from pre– and post–IDEA Institute on AI surveys</hd> <p>To compare their learning, the Fellows' perception of their knowledge and skills was collected using the same set of 12 questions before the onboarding program (pre–IDEA Institute on AI Survey) and after the week-long IDEA Institute on AI was completed (post–IDEA Institute on AI Survey). Fellows were asked to rate their perceived knowledge and understanding of AI on a scale of 1 to 5 (1 = Not at All; 5 = Very Much).</p> <p>Table 3 shows the pre– and post–IDEA Institute on AI surveys completed by both cohorts in 2021 and 2022. Improvements in the Fellows' learning about AI before and after completing the IDEA Institute on AI's program were evidenced in the mean score (<emph>M</emph>) and standard deviation (<emph>SD</emph>) scores.</p> <p>Table 3: 2021 and 2022 Fellows' pre– and post–IDEA Institute on AI survey results</p> <p> <ephtml> <table><colgroup span="1"><col align="left" span="1" /><col align="char" char="." span="1" /><col align="char" char="." span="1" /><col align="char" char="." span="1" /><col align="char" char="." span="1" /></colgroup><thead><tr><th align="left" rowspan="1" colspan="1">Questions</th><th align="center" rowspan="1" colspan="1">2021 Pre–survey <italic>M (SD)</italic> (<italic>n</italic> = 16)</th><th align="center" rowspan="1" colspan="1">2021 Post–survey <italic>M (SD)</italic> (<italic>n</italic> = 17)</th><th align="center" rowspan="1" colspan="1">2022 Pre–survey <italic>M (SD)</italic> (<italic>n</italic> = 20*)</th><th align="center" rowspan="1" colspan="1">2022 Post–survey <italic>M (SD)</italic> (<italic>n</italic> = 18)</th></tr></thead><tbody><tr><td align="left" rowspan="1" colspan="1">1. I understand the ethical aspects of AI.</td><td align="center" rowspan="1" colspan="1">3.81 (0.75)</td><td align="center" rowspan="1" colspan="1">4.65 (0.60)</td><td align="center" rowspan="1" colspan="1">3.4 (0.66)</td><td align="center" rowspan="1" colspan="1">4.67 (0.49)</td></tr><tr><td align="left" rowspan="1" colspan="1">2. I understand the social implications of AI.</td><td align="center" rowspan="1" colspan="1">3.81 (0.75)</td><td align="center" rowspan="1" colspan="1">4.41 (0.80)</td><td align="center" rowspan="1" colspan="1">3.35 (0.65)</td><td align="center" rowspan="1" colspan="1">4.67 (0.49)</td></tr><tr><td align="left" rowspan="1" colspan="1">3. I understand AI as a transformative technology.</td><td align="center" rowspan="1" colspan="1">4.00 (1.03)</td><td align="center" rowspan="1" colspan="1">4.71 (0.77)</td><td align="center" rowspan="1" colspan="1">3.70 (0.90)</td><td align="center" rowspan="1" colspan="1">4.89 (0.32)</td></tr><tr><td align="left" rowspan="1" colspan="1">4. I have core and foundational knowledge of AI.</td><td align="center" rowspan="1" colspan="1">2.75 (0.86)</td><td align="center" rowspan="1" colspan="1">4.18 (0.88)</td><td align="center" rowspan="1" colspan="1">2.5 (0.97)</td><td align="center" rowspan="1" colspan="1">4.50 (0.71)</td></tr><tr><td align="left" rowspan="1" colspan="1">5. I have knowledge of user experience (UX) methodology.</td><td align="center" rowspan="1" colspan="1">3.38 (0.96)</td><td align="center" rowspan="1" colspan="1">4.12 (0.70)</td><td align="center" rowspan="1" colspan="1">2.95 (0.97)</td><td align="center" rowspan="1" colspan="1">4.11 (0.76)</td></tr><tr><td align="left" rowspan="1" colspan="1">6. I know how to identify AI library products.</td><td align="center" rowspan="1" colspan="1">3.31 (0.81)</td><td align="center" rowspan="1" colspan="1">4.06 (0.03)</td><td align="center" rowspan="1" colspan="1">2.35 (1.15) </td><td align="center" rowspan="1" colspan="1">4.28 (0.75)</td></tr><tr><td align="left" rowspan="1" colspan="1">7. I know how to assess AI library products.</td><td align="center" rowspan="1" colspan="1">2.56 (0.81)</td><td align="center" rowspan="1" colspan="1">3.76 (0.97)</td><td align="center" rowspan="1" colspan="1">1.90 (1.04)</td><td align="center" rowspan="1" colspan="1">4.11 (0.68)</td></tr><tr><td align="left" rowspan="1" colspan="1">8. I know how to select AI library products.</td><td align="center" rowspan="1" colspan="1">2.25 (0.58)</td><td align="center" rowspan="1" colspan="1">3.59 (1.00)</td><td align="center" rowspan="1" colspan="1">1.75 (0.89)</td><td align="center" rowspan="1" colspan="1">4.06 (0.64)</td></tr><tr><td align="left" rowspan="1" colspan="1">9. I know how to apply AI technology to solve a library problem.</td><td align="center" rowspan="1" colspan="1">2.25 (1.18)</td><td align="center" rowspan="1" colspan="1">3.82 (1.07)</td><td align="center" rowspan="1" colspan="1">2.10 (1.04)</td><td align="center" rowspan="1" colspan="1">3.83 (0.51)</td></tr><tr><td align="left" rowspan="1" colspan="1">10. I have technical knowledge of developing an AI solution for a library.</td><td align="center" rowspan="1" colspan="1">2.19 (1.04)</td><td align="center" rowspan="1" colspan="1">3.59 (0.87)</td><td align="center" rowspan="1" colspan="1"> 1.80 (0.75)</td><td align="center" rowspan="1" colspan="1">3.88 (0.86)</td></tr><tr><td align="left" rowspan="1" colspan="1">11. I have technical knowledge of machine learning algorithms.</td><td align="center" rowspan="1" colspan="1">2.13 (0.96)</td><td align="center" rowspan="1" colspan="1">3.24 (0.90) </td><td align="center" rowspan="1" colspan="1">1.85 (1.01) </td><td align="center" rowspan="1" colspan="1">3.50 (1.20)</td></tr><tr><td align="left" rowspan="1" colspan="1">12. I know how to prepare data for AI projects.</td><td align="center" rowspan="1" colspan="1">2.38 (0.89)</td><td align="center" rowspan="1" colspan="1">3.71 (1.11)</td><td align="center" rowspan="1" colspan="1">2.05 (1.2)</td><td align="center" rowspan="1" colspan="1">3.67 (1.14)</td></tr><tr><td align="left" rowspan="1" colspan="1">Grand Mean (GM) Score</td><td align="center" rowspan="1" colspan="1">2.90</td><td align="center" rowspan="1" colspan="1">3.99</td><td align="center" rowspan="1" colspan="1">2.48</td><td align="center" rowspan="1" colspan="1">4.18</td></tr></tbody></table> </ephtml> </p> <p>1 <emph>Note</emph>. One participant could not participate in the ILS AI IDEA Institute on AI program but completed the pre-questionnaire. Scale: 1 to 5 (1 = Not at All; 5 = Very Much).</p> <hd id="AN0188368706-16">Fellows' expectations and reflections on learning experiences</hd> <p>In the open-ended section of the 2021 and 2022 Fellows' surveys, they commented on their expectations and learning experiences. In the pre–IDEA Institute on AI survey, they were asked about their expectations in participating in the IDEA Institute on AI, while in the post–IDEA Institute on AI survey, they elaborated on what they had learned.</p> <p>In the 2021 pre–IDEA Institute on AI survey, many strongly desired to acquire technical knowledge about AI tools, Python, and ML. In the post–IDEA Institute on AI survey, they shared their knowledge level of AI. For example, Fellow 21-12 noted, "I feel like I have a good understanding of the ethical and social implications of AI from readings like 'Algorithms of Oppression' and 'Data Feminism' and the film 'Coded Bias.' I'm also familiar with LIS (Library and Information Science) applications of AI from webinars and articles, but I'm lacking the technical knowledge ...."</p> <p>In the 2021 post–IDEA Institute on AI survey, the Fellows commented on learning technical aspects of AI. For example, Fellow 21-1 mentioned, "I learned about text and image analysis, the ethical considerations of AI, chatbots, algorithms, and so much more."</p> <p>Fellows greatly valued the learning experience they gained from their peer Fellows during the IDEA Institute on AI. Fellow 21-14 reported, "It was also interesting to learn about specific applications of AI from other fellows. Even though we all work in different service areas, I'm excited to share ideas with my colleagues in other library departments."</p> <p>Fellows highlighted learning from each other by sharing their ideas. For example, Fellow 21-15 noted, "I was inspired by the peer librarians on AI solutions in the library and information industry. I shared what I had learned from the IDEA Institute on AI with my colleagues in an informal presentation, and they felt as astonished as I did." They also appreciated learning about AI implementation, such as equity, diversity, inclusion, and accessibility (EDIA).</p> <p>In the 2022 pre–IDEA Institute on AI survey, consistent with the 2021 Fellows, the Fellows expected to gain technical knowledge of AI, exemplified by Fellow 22-3, who stated, "My areas of growth would be the technical knowledge of machine learning algorithms." Fellows highlighted the knowledge gap between understanding AI and using AI. Fellow 22-10 commented, "I have a general understanding of what AI is and what it can do. I don't have any practical knowledge of how to use or implement AI or machine learning." In addition, the Fellows were enthusiastic about examining AI applications in libraries, as Fellow 22-1 stated: "I am looking forward to looking at AI from a library-focused lens."</p> <p>The post–IDEA Institute on AI survey showed that the 2022 Fellows highly valued their learning of applying AI in libraries and had developed an understanding of implementing AI tools in library settings. Fellow 22-10 said, "The IDEA Institute on AI helped me to build perspective on finding, developing, applying, and maintaining AI systems in a library." Fellows also emphasized their deepened technical knowledge: "Before attending the IDEA Institute on AI, AI applications in libraries were vague to me. But now, I have a wider understanding of AI and ML applications in libraries. The program was very successful and very well prepared" (Fellow 22-4). Additionally, the Fellows realized the need for additional learning to support future technical implementation, as Fellow 22-16 mentioned: "I think I have a stronger knowledge of AI applications in libraries compared to what I had coming into the IDEA Institute on AI, but I also feel like it is a situation where the more I learn, the more I realize what I don't know ...."</p> <p>Some Fellows highlighted the significance of networking opportunities during the IDEA Institute on AI. For example, Fellow 22-3 mentioned "allowing me to make new connections with colleagues at other academic institutions across the country who share a common interest in the use of AI for supporting workflows in academic libraries."</p> <hd id="AN0188368706-17">Fellows' six-month post–IDEA Institute on AI survey</hd> <p>The Fellows took the survey six months after completing the IDEA Institute on AI and rated their confidence in their abilities to perform various AI tasks in the workplace on a scale of 1 to 5 (1 = Not at All; 5 = Very Much).</p> <p>According to the 2021 six-month survey results, the Fellows' confidence ratings on average across all 11 items were high (<emph>M</emph> = 4.03). As shown in Table 4, their rating was equal to or greater than moderately confident (<emph>M</emph> ≥ 4) on seven items. They demonstrated higher confidence in sharing AI knowledge, identifying an AI application or solution, and evaluating available AI products. On the other hand, their confidence ratings were between somewhat and slightly confident (3 ≤ <emph>M</emph> <4) on developing and applying AI solutions independently, addressing EDIA in AI applications, mitigating ethical issues associated with AI applications, and applying and effectively managing an AI solution.</p> <p>Table 4: 2021 and 2022 Fellows' six-month post–IDEA Institute on AI survey results</p> <p> <ephtml> <table><colgroup span="1"><col align="left" span="1" /><col align="char" char="." span="1" /><col align="char" char="." span="1" /></colgroup><thead><tr><th align="left" rowspan="1" colspan="1">Questions</th><th align="center" rowspan="1" colspan="1">2021 Fellows <italic>M (SD)</italic> (<italic>n</italic> = 17)</th><th align="center" rowspan="1" colspan="1">2022 Fellows <italic>M (SD)</italic> (<italic>n</italic> = 16)</th></tr></thead><tbody><tr><td align="left" rowspan="1" colspan="1">1. Identify an AI application or solution.</td><td align="center" rowspan="1" colspan="1">4.24 (0.83)</td><td align="center" rowspan="1" colspan="1">3.94 (0.85) </td></tr><tr><td align="left" rowspan="1" colspan="1">2. Evaluate available AI products.</td><td align="center" rowspan="1" colspan="1">4.18 (1.07)</td><td align="center" rowspan="1" colspan="1">3.56 (0.73)</td></tr><tr><td align="left" rowspan="1" colspan="1">3. Select appropriate AI products to implement.</td><td align="center" rowspan="1" colspan="1">4.12 (1.06)</td><td align="center" rowspan="1" colspan="1">3.63 (0.72)</td></tr><tr><td align="left" rowspan="1" colspan="1">4. Develop an AI solution independently.</td><td align="center" rowspan="1" colspan="1">3.53 (1.12)</td><td align="center" rowspan="1" colspan="1">2.75 (0.77)</td></tr><tr><td align="left" rowspan="1" colspan="1">5. Develop an AI solution collaboratively.</td><td align="center" rowspan="1" colspan="1">4.24 (0.90)</td><td align="center" rowspan="1" colspan="1">3.69 (0.70)</td></tr><tr><td align="left" rowspan="1" colspan="1">6. Apply user-centered design methodology in AI solutions.</td><td align="center" rowspan="1" colspan="1">4.06 (1.03)</td><td align="center" rowspan="1" colspan="1">3.81 (0.66)</td></tr><tr><td align="left" rowspan="1" colspan="1">7. Address equity, diversity, inclusion, and accessibility in AI applications.</td><td align="center" rowspan="1" colspan="1">3.82 (1.29)</td><td align="center" rowspan="1" colspan="1">4.06 (0.57)</td></tr><tr><td align="left" rowspan="1" colspan="1">8. Mitigate ethical issues associated with AI applications.</td><td align="center" rowspan="1" colspan="1">3.94 (1.20)</td><td align="center" rowspan="1" colspan="1">3.75 (0.78)</td></tr><tr><td align="left" rowspan="1" colspan="1">9. Apply and effectively manage an AI solution.</td><td align="center" rowspan="1" colspan="1">3.82 (1.29)</td><td align="center" rowspan="1" colspan="1">3.19 (0.75)</td></tr><tr><td align="left" rowspan="1" colspan="1">10. Share AI knowledge with colleagues.</td><td align="center" rowspan="1" colspan="1">4.41 (0.87)</td><td align="center" rowspan="1" colspan="1">4.06 (1.0)</td></tr><tr><td align="left" rowspan="1" colspan="1">11. Tackle any AI-related work tasks, in general.</td><td align="center" rowspan="1" colspan="1">4.0 (1.0)</td><td align="center" rowspan="1" colspan="1">3.13 (0.96)</td></tr><tr><td align="left" rowspan="1" colspan="1">Grand Mean (GM) Score</td><td align="center" rowspan="1" colspan="1">4.03</td><td align="center" rowspan="1" colspan="1">3.60</td></tr></tbody></table> </ephtml> </p> <p>2 <emph>Note</emph>. Scale: 1 = Not at All; 5 = Very Much.</p> <p>The 2022 Fellows' overall confidence (<emph>M</emph> = 3.60) in their abilities to perform various AI tasks was lower than that of the 2021 Fellows. While the 2022 Fellows reported a higher confidence level regarding addressing EDIA in applications and sharing AI knowledge with colleagues, they indicated that their confidence level was particularly lower concerning developing an AI solution independently, tackling any AI-related work tasks in general, and applying and effectively managing an AI solution, as shown in Table 4.</p> <p>The six-month post–IDEA Institute on AI survey included four open-ended questions about the IDEA Institute on AI's impact on learning, professional development, and overall experience with AI solutions and workplace applications. The 2021 Fellows' comments yielded five themes.</p> <hd id="AN0188368706-18">Theme 1: Self-confidence in understanding AI.</hd> <p>Fellows reported that attending the IDEA Institute on AI enhanced their confidence in their overall understanding of AI and AI tools. For example, Fellow 21-16 described, "I now have a significantly deeper conceptual understanding, and enough hands-on skill that I know *how* to develop the skills for any project I want to take on. Before, I really didn't know where to start if I wanted to learn how to use AI or machine learning."</p> <hd id="AN0188368706-19">Theme 2: AI knowledge in the workplace.</hd> <p>Fellows expressed an enhanced understanding of applying AI in the workplace. For example, Fellow 21-17 reported, "The AI IDEA Institute on AI exposed me to how AI was being utilized in libraries and what possibilities that AI could help bring to fruition."</p> <hd id="AN0188368706-20">Theme 3: Idea sharing and connecting with other Fellows.</hd> <p>The Fellows highlighted the significance of networking opportunities during the IDEA Institute on AI. For example, Fellow 21-9 said, "The activities and project times provided an opportunity to meet with fellows of various backgrounds and expertise to learn from them and share ideas, which encouraged me even more."</p> <hd id="AN0188368706-21">Theme 4: AI projects in the workplace.</hd> <p>The Fellows indicated they felt equipped with the necessary tools and confidence to initiate AI projects upon returning to their workplaces. For example, Fellow 21-9 said, "I have gained a greater understanding of the capabilities of AI as well as the areas in which AI may not be as effective with its current limitations. This has brought me to a place where I can lead projects and discussions on the possibilities of incorporating AI within our library system with confidence. I am by no means an expert in all things related to AI...."</p> <p>The Fellows expressed their ability to guide team members on AI application projects in their workplace. For example, Fellow 21-15 said, "When leading the team on developing an AI-driven application, I can foresee the exact outcome that will be created and tell what type of algorithms should be applied in its matching component. In addition, I can guide the team members on finding proper training materials and conduct highly efficient self-learning."</p> <hd id="AN0188368706-22">Theme 5: Awareness of EDIA issues in AI implementation.</hd> <p>Fellows appreciated the IDEA Institute on AI's attention to EDIA issues, as Fellow 21-17 mentioned: "I really appreciated the DEIA component of the IDEA Institute on AI, because I learned of ways to incorporate DEIA in all phases of an AI-related technology project. Moreover, I think, the cohort, speakers, and organizers of the IDEA Institute on AI were and sharing updates about what is going on in their work and in the field."</p> <p>The 2022 Fellows' comments in the six-month post-IDEA Institute on AI survey also revealed five themes, of which one theme was the same as in 2021 (AI projects in the workplace).</p> <hd id="AN0188368706-23">Theme 1: Familiarity with AI technology.</hd> <p>After participating in the IDEA Institute on AI, the Fellows became more familiar and comfortable with AI technology. For example, Fellow 22-5 wrote, "I think that the [IDEA Institute on AI] contributed to my professional development in multiple ways, including by allowing me to build my technical skills."</p> <hd id="AN0188368706-24">Theme 2: AI implementation in the workplace.</hd> <p>Fellows conveyed an improved understanding of applying AI in their library workplaces. For example, Fellow 22-14 mentioned, "I now understand better what is feasible to implement in my workplace and what can be done with AI, without asking for new resources or additional funding."</p> <hd id="AN0188368706-25">Theme 3: AI projects in the workplace.</hd> <p>Some Fellows were eager to implement AI projects in their workplace. For example, Fellow 22-4 mentioned initiating two projects in their library: "[I] opened up potential projects for remediating metadata; [and] developing use cases for AI solutions in FOLIO." (FOLIO is an open-source library services platform.)</p> <hd id="AN0188368706-26">Theme 4: New connections with Fellows.</hd> <p>Fellows highlighted the significance of building new connection opportunities during the IDEA Institute on AI and emphasized the value of engaging with peers and experts, fostering a collaborative environment that enriched their learning experiences. For example, Fellow 22-5 said, "I also really enjoyed participating and having the opportunity to make new connections with colleagues in roles similar to my own at other academic institutions. I found it inspiring and insightful to hear from other members of the cohort about their experiences at their universities and how they hoped to utilize AI in their work."</p> <hd id="AN0188368706-27">Theme 5: Awareness of ethical issues in AI implementation.</hd> <p>Fellows acknowledged gaining awareness of the ethical considerations and potential biases associated with AI implementation, as Fellow 22-5 wrote: "I think that the [IDEA Institute on AI] contributed to my professional development in multiple ways, including by allowing me to build my technical skills, giving me greater insight into the ethical issues associated with AI."</p> <p>In summary, the 2021 and 2022 Fellows could use the AI knowledge and skills gained from the IDEA Institute on AI, became aware of various ethical and EDIA challenges in applying AI, and felt more confident initiating and guiding AI projects in their workplaces.</p> <hd id="AN0188368706-28">Supervisors' perceptions of Fellows' AI learning in the IDEA Institute on AI</hd> <p>The Fellows' supervisors were invited to complete the six-month post–IDEA Institute on AI survey on their perceptions of the Fellows' learning of AI at the IDEA Institute on AI. The supervisors rated the Fellows' abilities to perform various AI-related activities in the workplace on a scale of 1 to 5 (1 = Not at All; 5 = Very Much). As shown in Table 5, the mean scores of the 2021 Fellows ranged from 4.56 to 5, indicating high proficiencies in performing various AI-related tasks. The supervisors' grand mean score (GM) rating score on the 11 survey items was also high (<emph>GM </emph>= 4.83). The supervisors' rating of their 2022 Fellows' capabilities in performing AI-related tasks ranged from a mean score of 4.10 to 4.73, with a grand mean rating score of 4.47, indicating satisfactory proficiencies across 11 questions.</p> <p>Table 5: 2021 and 2022 supervisors' six-month post–IDEA Institute on AI survey results</p> <p> <ephtml> <table><colgroup span="1"><col align="left" span="1" /><col align="char" char="." span="1" /><col align="char" char="." span="1" /></colgroup><thead><tr><th align="left" rowspan="1" colspan="1">Questions</th><th align="center" rowspan="1" colspan="1">2021 supervisors <italic>M (SD)</italic></th><th align="center" rowspan="1" colspan="1">2022 supervisors <italic>M (SD)</italic></th></tr></thead><tbody><tr><td align="left" rowspan="1" colspan="1">1. Identify an AI application or solution.</td><td align="center" rowspan="1" colspan="1">5.00 (0.0)<xref ref-type="table-fn" rid="tfn4">a</xref> </td><td align="center" rowspan="1" colspan="1">4.73 (0.47)<xref ref-type="table-fn" rid="tfn8">e</xref></td></tr><tr><td align="left" rowspan="1" colspan="1">2. Evaluate available AI products.</td><td align="center" rowspan="1" colspan="1">4.90 (0.32)<xref ref-type="table-fn" rid="tfn4">a</xref></td><td align="center" rowspan="1" colspan="1">4.55 (0.69)<xref ref-type="table-fn" rid="tfn8">e</xref></td></tr><tr><td align="left" rowspan="1" colspan="1">3. Select appropriate AI products to implement.</td><td align="center" rowspan="1" colspan="1">5 (0.0)<xref ref-type="table-fn" rid="tfn5">b</xref></td><td align="center" rowspan="1" colspan="1">4.27 (0.65)<xref ref-type="table-fn" rid="tfn8">e</xref></td></tr><tr><td align="left" rowspan="1" colspan="1">4. Develop an AI solution independently.</td><td align="center" rowspan="1" colspan="1">4.56 (0.72)<xref ref-type="table-fn" rid="tfn5">b</xref></td><td align="center" rowspan="1" colspan="1">4.30 (0.68)<xref ref-type="table-fn" rid="tfn9">f</xref></td></tr><tr><td align="left" rowspan="1" colspan="1">5. Develop an AI solution collaboratively.</td><td align="center" rowspan="1" colspan="1">4.78 (0.44)<xref ref-type="table-fn" rid="tfn5">b</xref></td><td align="center" rowspan="1" colspan="1">4.50 (0.71)<xref ref-type="table-fn" rid="tfn9">f</xref></td></tr><tr><td align="left" rowspan="1" colspan="1">6. Apply user-centered design methodology in AI solutions.</td><td align="center" rowspan="1" colspan="1">4.89 (0.33)<xref ref-type="table-fn" rid="tfn5">b</xref></td><td align="center" rowspan="1" colspan="1">4.56 (0.73)<xref ref-type="table-fn" rid="tfn9">f</xref></td></tr><tr><td align="left" rowspan="1" colspan="1">7. Address equity, diversity, inclusion, and accessibility in AI applications.</td><td align="center" rowspan="1" colspan="1">4.88 (0.35)<xref ref-type="table-fn" rid="tfn5">b</xref></td><td align="center" rowspan="1" colspan="1">4.64 (0.50)<xref ref-type="table-fn" rid="tfn8">e</xref></td></tr><tr><td align="left" rowspan="1" colspan="1">8. Mitigate ethical issues associated with AI applications.</td><td align="center" rowspan="1" colspan="1">4.67 (0.2)<xref ref-type="table-fn" rid="tfn5">b</xref></td><td align="center" rowspan="1" colspan="1">4.30 (0.67)<xref ref-type="table-fn" rid="tfn8">e</xref></td></tr><tr><td align="left" rowspan="1" colspan="1">9. Apply and effectively manage an AI solution.</td><td align="center" rowspan="1" colspan="1">4.75 (0.46)<xref ref-type="table-fn" rid="tfn6">c</xref></td><td align="center" rowspan="1" colspan="1">4.10 (0.57)<xref ref-type="table-fn" rid="tfn9">f</xref></td></tr><tr><td align="left" rowspan="1" colspan="1">10. Share AI knowledge with colleagues.</td><td align="center" rowspan="1" colspan="1">4.90 (0.32)<xref ref-type="table-fn" rid="tfn4">a</xref></td><td align="center" rowspan="1" colspan="1">4.67 (0.49)<xref ref-type="table-fn" rid="tfn7">d</xref></td></tr><tr><td align="left" rowspan="1" colspan="1">11. Tackle any AI-related work tasks, in general.</td><td align="center" rowspan="1" colspan="1">4.78 (0.44)<xref ref-type="table-fn" rid="tfn5">b</xref></td><td align="center" rowspan="1" colspan="1">4.58 (0.51)<xref ref-type="table-fn" rid="tfn7">d</xref></td></tr><tr><td align="left" rowspan="1" colspan="1">Grand Mean (GM) Score</td><td align="center" rowspan="1" colspan="1">4.83</td><td align="center" rowspan="1" colspan="1">4.47</td></tr></tbody></table> </ephtml> </p> <ulist> <item>3 <emph>Note</emph>. Scale: 1 = Not at All; 5 = Very Much.</item> <item>4 a <emph>n</emph> = 10.</item> <item>5 b <emph>n</emph> = 9.</item> <item>6 c <emph>n</emph> = 8.</item> <item>7 d <emph>n</emph> = 12.</item> <item>8 e <emph>n</emph> = 11.</item> <item>9 f <emph>n</emph> = 10.</item> </ulist> <p>The 2021 and 2022 supervisors also provided qualitative comments on their Fellows' AI skills and knowledge learned from the IDEA Institute on AI through four open-ended questions. The 2021 supervisors commented on their Fellows' AI applications in library settings, collaboration opportunities, and benefits gained from the professional development opportunity. For example, Supervisor 21-7 reported that their Fellow "learned more of the available tools and the library-specific applications of AI. The individual came from a more technical/less library background, so the applications to the library setting were particularly valuable."</p> <p>In addition, the supervisors described how their Fellows collaborated with different departments within their community on AI-related initiatives and shared AI knowledge with colleagues. Supervisor 21-1 said, "The Fellow is gifted in making new concepts and ideas visible to her colleagues. She was able to push out the ideas she's learned from the IDEA Institute on AI through library-wide forums and workshops."</p> <p>Supervisors pointed out that their Fellows benefited from the networking opportunities with peers from other libraries, as Supervisor 21-8 noted: "The IDEA Institute on AI contributed to [the Fellow's] professional development by creating an outlet where she was able to freely exchange ideas relating to emerging technology and AI. She also built new relationships with colleagues at peer institutions allowing the possibility for future collaborations and knowledge exchange."</p> <p>The 2022 supervisors commented on their Fellows' abilities to start AI-related projects and collaborations with their colleagues. Supervisor 22-1 noted, "[The Fellow] is currently working with our staff to leverage AI textual analytics for our [X] Magazine online resource. We are hopeful that, through experimentation, we can pull data that will impact scholarship by identifying resources in our Archives that researchers have a specific need to access." They also valued the improved collaboration of their Fellows with relevant departments within their institutions. For example, Supervisor 22-4 said, "[the Fellow] has been able to identify appropriate collaborators and campus partners, as well as identify and explain the need for different stakeholders and expertise, in a project related to using AI in the library context."</p> <p>Supervisors highlighted the networking opportunities the Fellows built with other institutions during the 2022 IDEA Institute on AI as a valuable contribution of the IDEA Institute on AI. Supervisor 22-8 stated, "[The Fellow] has continued his self-guided AI education by staying in regular communication with several other 2022 Fellows, who share their developments and discoveries," and gained more confidence in his abilities to use and implement AI. In addition, supervisors mentioned that the 2022 Fellows displayed curiosity, thoughtfulness, and creativity, positioning them as creative thinkers and visionaries. For example, Supervisor 22-5 indicated, "[The Fellow] is curious and thoughtful about creating and applying scripts to situations that will reduce repetitive tasks or manual work to mitigate human error/ensure consistency."</p> <p>These supervisors' positive feedback about their 2021 and 2022 Fellows indicates that the IDEA Institute on AI benefited the Fellows, their library and information environments, and their colleagues. The program helped the Fellows gain AI knowledge and skills and supported their professional growth.</p> <hd id="AN0188368706-29">Discussion</hd> <p>AI technologies present numerous opportunities for libraries to evolve and adapt rapidly across various library services and operations. However, AI brings challenges to libraries, including ethical considerations surrounding privacy, bias in algorithms, and changes in information access and management. Given the profound impact of AI on libraries, there is a significant need to offer AI education and training programs for library and information professionals. Prior research (e.g., [<reflink idref="bib22" id="ref38">22</reflink>]; [<reflink idref="bib27" id="ref39">27</reflink>]) showed that most library and information professionals expressed interest in AI education and training programs but found it difficult to acquire AI-related knowledge and skills within their current job responsibilities and work environments.</p> <p>The IDEA Institute on AI, offered in 2021 and 2022, was designed to meet the demand for AI education and training among library and information professionals. The program evaluation's findings indicate that it is an exemplary professional development program. Three primary factors contributed to the IDEA Institute on AI's success: the knowledge and skills that library and information professionals gained from it, pedagogical approaches that fostered an interactive and collaborative learning environment, and genuine support from the Fellows' supervisors.</p> <p>The first research question addressed in this study was <emph>To what extent did the program advance the knowledge and skills of library and information professionals?</emph>[<reflink idref="bib11" id="ref40">11</reflink>] highlighted the transition in the role of librarians from mediating information to encompassing a comprehensive grasp of data and AI, including competencies in computational methods, data programming, management, and preservation. After completing the IDEA Institute on AI, Fellows who participated in the IDEA Institute on AI reported a higher level of understanding of AI, particularly its ethical aspects, social implications, and transformative potential. Before the pre–IDEA Institute on AI onboarding program, Fellows perceived their AI knowledge and skills as much lower in areas such as technical proficiency in developing and implementing AI solutions, understanding ML algorithms, preparing data for AI projects, and selecting and assessing AI solutions and tools as compared to after the IDEA Institute on AI. As a result of attending the IDEA Institute on AI, Fellows reported substantial improvements in their perceived technical knowledge of AI. In the post–IDEA Institute on AI survey, Fellows reported positive feedback regarding their level of core and foundational knowledge of AI.</p> <p>The notable improvements in the Fellows' technical knowledge of AI are attributed to the collaborative learning environment the PIs cultivated, wherein Fellows felt empowered to share and enhance their technical skills. Recognizing the diverse technical backgrounds and varying pace of learning among the Fellows from the outset, the PIs facilitated peer learning throughout the IDEA Institute on AI by incorporating ample discussion sessions and hands-on activities in addition to the lectures and allowing time for the Fellows to work on their project with peer fellows and instructors.</p> <p>Building upon feedback from the 2021 IDEA Institute on AI, for the 2022 IDEA Institute on AI, the PIs assigned Fellows to four small groups considering their diverse backgrounds and technical expertise, ensuring an even greater level of collegiality and support among group members.</p> <p>Regarding the research question <emph>How do the Fellows perceive their learning of AI from the program?</emph>, the analyzed data gathered from the open-ended questions in both the post–IDEA Institute on AI and six-month surveys yielded valuable insights. Fellows reported that their participation in the IDEA Institute on AI helped them understand the technical aspects of AI, including initiating new AI projects, identifying AI applications or solutions, and developing AI solutions collaboratively. However, they still lacked confidence in developing AI solutions individually. In addition, Fellows gained an increased awareness of EDIA issues related to AI implementation in the workplace.</p> <p>Overall, the IDEA Institute on AI contributed to broadening the Fellows' understanding of AI issues, encompassing various perspectives beyond technical competencies, which matched the goals of the IDEA Institute on AI.</p> <p>After attending the IDEA Institute on AI, collecting feedback from the Fellows' supervisors was crucial to better understanding how the Fellows contributed to their workplaces regarding AI projects. The research question <emph>How do the workplace supervisors perceive the Fellows' learning of AI from the program?</emph> generated valuable feedback, reflecting on the value of the IDEA Institute on AI, particularly initiating collaboration on AI projects within and outside their libraries and sharing their AI knowledge with colleagues.</p> <hd id="AN0188368706-30">Conclusion</hd> <p>The results of this case study have significant implications for developing future professional development programs in the LIS field. To achieve the objective of cultivating a community of AI leaders among library and information professionals, the PIs ensured that throughout the IDEA Institute on AI, Fellows were guided and encouraged to articulate their perspectives on AI, engage in networking opportunities, and share their plans with other Fellows regarding how they would apply AI solutions in their workplaces. By fostering such collaborative learning environments, the Fellows could establish a community of practice. As a result, after participating in the IDEA Institute on AI, Fellows have advanced as AI leaders, building AI-powered chatbots for their libraries, creating chatbots for reference interactions, presenting AI training in Qatar and Taiwan and a webinar in Egypt, participating in national and international professional conferences facilitated by the PIs (such as the ALA annual conference, ASIS&T annual meeting, and Tennessee Library Association conference, as well as others on their own), publishing papers and book chapters, and contributing to AI education initiatives.</p> <p>From the pre–IDEA Institute on AI recruitment of Fellows to sessions held during the IDEA Institute on AI and through the post–IDEA Institute on AI mentoring activities, EDIA played a pivotal role in the success of the IDEA Institute on AI, starting with the promotion and recruitment process. The EDIA theme surfaced consistently whenever Fellows reported on their learning experiences during and after the IDEA Institute on AI. Consequently, the program evaluation revealed that Fellows developed a holistic understanding of AI within the context of EDIA. The IDEA Institute on AI equipped the Fellows with core and foundational knowledge and skills in various aspects of AI. The fact that most Fellows needed additional training to develop AI projects individually suggests that future professional development programs in AI must provide substantial training in coding skills, data preparation, and awareness and use of AI tools.</p> <p>The continuous evolution of AI technology presents opportunities and challenges for education and professional development programs aimed at library and information professionals. When the PIs created the curriculum for the 2021 and 2022 IDEA Institute on AI, generative AI and large language models (LLMs) were seldom discussed in society and library settings. However, since the completion of the IDEA Institute on AI in the summer of 2022, generative AI emerged in November 2022 as one of the most influential and challenging technologies for all sectors of society, including libraries. Although ChatGPT answers to reference questions revealed that it is not able to provide satisfactory responses to various types of reference inquiries ([<reflink idref="bib18" id="ref41">18</reflink>]), large language models and generative AI systems are expected to impact virtually all aspects of library services and operations, including reference services, search and discovery, acquisitions, cataloging, classification, interlibrary loan, circulation management, and book recommendations ([<reflink idref="bib17" id="ref42">17</reflink>]). The fast-paced development of AI highlights the crucial need to consistently offer professional development programs for library and information professionals, ensuring they stay updated on the emergence of new technologies. The ongoing delivery of AI training was incorporated in the IDEA Institute on AI's sustainability plan, which is being addressed by its partner ASIS&T, which has been committed to the future delivery of the IDEA Institute on AI since May 2024 (https://<ulink href="http://www.asist.org/?s=IDEA+Institute">www.asist.org/?s=IDEA+Institute</ulink>).</p> <p>In this case study, most Fellows who participated in the 2021 and 2022 cohorts were in academic libraries, with few from other library sectors. Following the release of ChatGPT in November 2022, all libraries became more concerned about the impact of generative AI. This is evident in the growing AI initiatives in public libraries (e.g., [<reflink idref="bib26" id="ref43">26</reflink>]), school libraries (e.g., [<reflink idref="bib19" id="ref44">19</reflink>].), and rural libraries (e.g., [<reflink idref="bib5" id="ref45">5</reflink>]). To address the preparation of library and information professionals across various sectors, training is needed to meet their common and distinct needs. Moreover, LIS programs should augment their curricula with offerings in various aspects of AI to prepare the next generation of LIS professionals. The curriculum should target not only the technical aspects of AI but also the ethical, social, and societal aspects and provide experiential learning opportunities to put AI learning into practice.</p> <hd id="AN0188368706-31">Acknowledgment</hd> <p>This project was funded by the Institute of Museum and Library Services grant RE-246419-OLS-20. We thank IMLS for supporting this project; the Institute's Advisory Board Members and Instructors: Nicole Coleman, Digital Research Architect, Stanford Libraries; Jiangen He, Assistant Professor, The University of Tennessee, School of Information Sciences; Claudia Engle, Academic Technology Specialist and Lecturer, Center for Interdisciplinary Digital Research, Stanford University; and Bill Mischo, Head of Grainger Engineering Library Information Center and Berthold Family Professor Emeritus in Information Access and Discovery, University of Illinois at Urbana-Champaign. We extend our gratitude to Claudia Gutierrez, 2021 Graduate Research Assistant, The University of Tennessee, School of Information Sciences, and Yujin Choi, 2022 Graduate Research Assistant, who is a co-author of this article, for their support and commitment to the success of the Institute.</p> <ref id="AN0188368706-32"> <title> References </title> <blist> <bibl id="bib1" idref="ref30" type="bt">1</bibl> <bibtext> Association for Information Science and Technology (ASIS&T), Association for Library and Information Science Education (ALISE), and the iSchools. (2020). Statement on AI ethics and the contributions of diverse voices in the discussion. https://<ulink href="http://www.asist.org/2020/12/21/ethics-in-ai-statement/">www.asist.org/2020/12/21/ethics-in-ai-statement/</ulink></bibtext> </blist> <blist> <bibl id="bib2" idref="ref11" type="bt">2</bibl> <bibtext> Bilal, D., Chu, C. M., Rieh, S. Y. (2022, March 4).. 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Public libraries set the stage for integration of artificial intelligence in their services and resources. https://<ulink href="http://www.urbanlibraries.org/newsroom/release-public-libraries-set-the-stage-for-integration-of-artificial-intelligence-in-their-services-and-resources">www.urbanlibraries.org/newsroom/release-public-libraries-set-the-stage-for-integration-of-artificial-intelligence-in-their-services-and-resources</ulink></bibtext> </blist> <blist> <bibtext> Yoon, J., Andrews, J. E., & Ward, H. L. (2022). Perceptions on adopting artificial intelligence and related technologies in libraries: Public and academic librarians in North America.. Library Hi Tech, 40(6), 1893–1915. https://doi.org/10.1108/LHT-07-2021-0229</bibtext> </blist> </ref> <aug> <p>By Dania Bilal; Clara M. Chu; Soo Young Rieh and Yujin Choi</p> <p>Reported by Author; Author; Author; Author</p> <p></p> <p>Dania Bilal is Patricia D. Williams Professor at the School of Information Sciences, University of Tennessee-Knoxville. Her research focuses on human information behavior and interaction with technology, AI in education, GenAI-4Youth, AI for information professionals, and student immersive learning experiences (ILEs). It also intersects human–AI interaction and information retrieval.</p> <p>Clara M. Chu is Mortenson Distinguished Professor and Director of the Mortenson Center for International Library Programs, University of Illinois Urbana-Champaign. Her research focuses on understanding the information needs, uses, and barriers faced by underserved communities to enhance equitable information access in an interconnected, technological global community.</p> <p>Soo Young Rieh is Interim Dean and Brooke E. Sheldon Professor of Management and Leadership in the School of Information at the University of Texas at Austin. Her research focuses on information credibility, search as learning, creativity support in search, information literacy, and the application of AI in library settings.</p> <p>Yujin Choi is a doctoral student in the School of Information at the University of Texas at Austin. Her research explores human learning, focusing on search as learning, creativity, and AI applications in libraries. She is a funded fellow and has contributed to funded projects on AI in libraries.</p> </aug> <nolink nlid="nl1" bibid="bib14" firstref="ref3"></nolink> <nolink nlid="nl2" bibid="bib22" firstref="ref4"></nolink> <nolink nlid="nl3" bibid="bib21" firstref="ref6"></nolink> <nolink nlid="nl4" bibid="bib24" firstref="ref7"></nolink> <nolink nlid="nl5" bibid="bib15" firstref="ref8"></nolink> <nolink nlid="nl6" bibid="bib13" firstref="ref9"></nolink> <nolink nlid="nl7" bibid="bib16" firstref="ref10"></nolink> <nolink nlid="nl8" bibid="bib23" firstref="ref12"></nolink> <nolink nlid="nl9" bibid="bib25" firstref="ref13"></nolink> <nolink nlid="nl10" bibid="bib10" firstref="ref16"></nolink> <nolink nlid="nl11" bibid="bib12" firstref="ref17"></nolink> <nolink nlid="nl12" bibid="bib27" firstref="ref19"></nolink> <nolink nlid="nl13" bibid="bib11" firstref="ref22"></nolink> <nolink nlid="nl14" bibid="bib20" firstref="ref28"></nolink> <nolink nlid="nl15" bibid="bib18" firstref="ref41"></nolink> <nolink nlid="nl16" bibid="bib17" firstref="ref42"></nolink> <nolink nlid="nl17" bibid="bib26" firstref="ref43"></nolink> <nolink nlid="nl18" bibid="bib19" firstref="ref44"></nolink>
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  Data: Bridging the AI Education, Knowledge, and Skills Gap of Library and Information Professionals: Evaluation of the Innovation, Inquiry, Disruption, and Access (IDEA) Institute on Artificial Intelligence
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  Data: <searchLink fieldCode="AR" term="%22Dania+Bilal%22">Dania Bilal</searchLink><br /><searchLink fieldCode="AR" term="%22Clara+M%2E+Chu%22">Clara M. Chu</searchLink><br /><searchLink fieldCode="AR" term="%22Soo+Young+Rieh%22">Soo Young Rieh</searchLink><br /><searchLink fieldCode="AR" term="%22Yujin+Choi%22">Yujin Choi</searchLink>
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  Data: <searchLink fieldCode="SO" term="%22Journal+of+Education+for+Library+and+Information+Science%22"><i>Journal of Education for Library and Information Science</i></searchLink>. 2025 66(4):340-362.
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  Data: Association for Library and Information Science Education. Available from: University of Toronto Press. 5201 Dufferin Street, Toronto, ON, M3H 5T8 Canada. Tel: 416-667–7929; Fax: 416-667–7832; e-mail: journals@utpress.utoronto.ca; e-mail: office@alise.org; Web site: https://www.utpjournals.press/loi/jelis
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  Data: 23
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  Label: Publication Date
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  Data: 2025
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  Data: Institute of Museum and Library Services (IMLS)
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  Data: RE246419OLS20
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  Data: <searchLink fieldCode="EL" term="%22Adult+Education%22">Adult Education</searchLink>
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  Data: <searchLink fieldCode="DE" term="%22Artificial+Intelligence%22">Artificial Intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Professional+Development%22">Professional Development</searchLink><br /><searchLink fieldCode="DE" term="%22Continuing+Education%22">Continuing Education</searchLink><br /><searchLink fieldCode="DE" term="%22Information+Scientists%22">Information Scientists</searchLink><br /><searchLink fieldCode="DE" term="%22Program+Effectiveness%22">Program Effectiveness</searchLink><br /><searchLink fieldCode="DE" term="%22Program+Attitudes%22">Program Attitudes</searchLink>
– Name: DOI
  Label: DOI
  Group: ID
  Data: 10.3138/jelis-2024-0033
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  Label: ISSN
  Group: ISSN
  Data: 0748-5786<br />2328-2967
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Artificial Intelligence (AI) is reshaping all sectors of society, including libraries. AI adoption in libraries has been gradual due to concerns and challenges, including ethical issues, maturity of the technology, insufficient AI education and training designed for library and information professionals, and gaps in AI education in library and information science (LIS) programs. This case study reports on the motivations, processes, and evaluations of the IDEA Institute on AI that was developed to equip two cohorts (Fellows) of information professionals who participated in the 2021 and 2022 IDEA Institute on AI with the foundational knowledge and skills to lead AI work. A multi-method approach was used to collect and analyze the evaluation data from multiple sources at different points of the IDEA Institute on AI. The IDEA Institute on AI applied an outcome-based planning and evaluation model and employed formative and summative evaluations using surveys and focus-group discussions. Fellows worked in various library and information environments, most in academic libraries. The case study results showed that the Fellows' AI knowledge and skills increased substantially, their confidence greatly increased upon completing the IDEA Institute on AI, and they engaged in AI projects in their workplaces. They built awareness of AI issues and challenges and developed a comprehensive understanding of AI within the context of equity, diversity, inclusion, and accessibility. The Fellows' supervisors were positive about the learning and experience their Fellows gained from the IDEA Institute on AI and their peers. The results of this case study have significant implications for developing AI professional development programs in the LIS field, providing exemplary AI education and training as AI technology evolves, including generative AI and large language models, and integrating AI into LIS curricula.
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  Data: EJ1485426
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        Value: 10.3138/jelis-2024-0033
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      – Text: English
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      Pagination:
        PageCount: 23
        StartPage: 340
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      – SubjectFull: Artificial Intelligence
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      – SubjectFull: Professional Development
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      – SubjectFull: Continuing Education
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      – SubjectFull: Information Scientists
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      – SubjectFull: Program Effectiveness
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      – SubjectFull: Program Attitudes
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      – TitleFull: Bridging the AI Education, Knowledge, and Skills Gap of Library and Information Professionals: Evaluation of the Innovation, Inquiry, Disruption, and Access (IDEA) Institute on Artificial Intelligence
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