View in EDS PDF Full Text

Development and Validation of the Artificial Intelligence Learning Intention Scale (AILIS) for University Students

Saved in:
Bibliographic Details
Title: Development and Validation of the Artificial Intelligence Learning Intention Scale (AILIS) for University Students
Language: English
Authors: Ching Sing Chai, Ding Yu (ORCID 0000-0002-0154-2691), Ronnel B. King, Ying Zhou (ORCID 0000-0001-5736-2094)
Source: SAGE Open. 2024 14(2).
Availability: SAGE Publications. 2455 Teller Road, Thousand Oaks, CA 91320. Tel: 800-818-7243; Tel: 805-499-9774; Fax: 800-583-2665; e-mail: journals@sagepub.com; Web site: https://sagepub.com
Peer Reviewed: Y
Page Count: 16
Publication Date: 2024
Document Type: Journal Articles
Reports - Research
Education Level: Higher Education
Postsecondary Education
Descriptors: Artificial Intelligence, Intention, Measures (Individuals), Development, Validity, College Students, Student Behavior, Foreign Countries, Student Attitudes, Universities
Geographic Terms: China, China (Beijing), China (Shanghai), China (Guangzhou)
DOI: 10.1177/21582440241242188
ISSN: 2158-2440
Abstract: As artificial intelligence (AI) permeates almost all aspects of our lives, university students need to acquire relevant knowledge, skills, and attitudes to adapt to the challenges it poses. This study reports the development and validation of a scale called the Artificial Intelligence Learning Intention Scale (AILIS). AILIS was designed to measure the different factors that shape university students' behavioral intentions to learn about AI and their AI learning. We recruited 907 Chinese university students who answered the survey. The scale is comprised of 9 factors that are categorized into various dimensions pertaining to epistemic capacity (AI basic knowledge, programming efficacy, designing AI for social good), facilitating environments (actual use of AI systems, subjective norms, access to support and technology), psychological attitudes (resilience, optimism, personal relevance), and focal outcomes (behavioral intention to learn AI, actual learning of AI). Reliability analyses and confirmatory factor analyses indicated that the scale has acceptable reliability and construct validity. Structural equational modeling results demonstrated the critical role played by epistemic capacity, facilitating environments, and psychological attitudes in promoting students' behavioral intentions and actual learning of AI. Overall, the findings revealed that university students express a strong intention to learn about AI, and this behavioral intention is positively associated with actual learning. The study contextualizes the theory of planned behavior for university AI education, provides guidelines on the design of AI curriculum courses, and proposes a possible tool to evaluate university AI curriculum.
Abstractor: As Provided
Notes: https://doi.org/10.6084/m9.figshare.23501316
Entry Date: 2024
Accession Number: EJ1433407
Database: ERIC
FullText Links:
  – Type: pdflink
    Url: https://content.ebscohost.com/cds/retrieve?content=AQICAHj0k_4E0hTGH8RJwT4gCJyBsGNe_WN95AvKlDbXJGqwxwFS9HNgWn28zuD9-O_dOh1hAAAA4zCB4AYJKoZIhvcNAQcGoIHSMIHPAgEAMIHJBgkqhkiG9w0BBwEwHgYJYIZIAWUDBAEuMBEEDCPpILz1gW6SFF4bZAIBEICBm9sdq-ZdJijbHW-jG3iKe--vsJOUGs1_cCNo-QIIs13ezxcvti5FVLvfh5nbvUPQZyJe2bHEfFDg2hfAEtWBuUk5_C3quJEuzUVsL_CAiUTi8Klh-8AS8f8kZT782NsFCxp5hoscg37rUL6n6DM3RJyK69ehEyeEhY8DzjFrdvrOmADQxQjVXqyfG7liCpKHnHAW5l4bVWW0mj6C
Text:
  Availability: 0
Header DbId: eric
DbLabel: ERIC
An: EJ1433407
AccessLevel: 3
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Development and Validation of the Artificial Intelligence Learning Intention Scale (AILIS) for University Students
– Name: Language
  Label: Language
  Group: Lang
  Data: English
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Ching+Sing+Chai%22">Ching Sing Chai</searchLink><br /><searchLink fieldCode="AR" term="%22Ding+Yu%22">Ding Yu</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-0154-2691">0000-0002-0154-2691</externalLink>)<br /><searchLink fieldCode="AR" term="%22Ronnel+B%2E+King%22">Ronnel B. King</searchLink><br /><searchLink fieldCode="AR" term="%22Ying+Zhou%22">Ying Zhou</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0001-5736-2094">0000-0001-5736-2094</externalLink>)
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="SO" term="%22SAGE+Open%22"><i>SAGE Open</i></searchLink>. 2024 14(2).
– Name: Avail
  Label: Availability
  Group: Avail
  Data: SAGE Publications. 2455 Teller Road, Thousand Oaks, CA 91320. Tel: 800-818-7243; Tel: 805-499-9774; Fax: 800-583-2665; e-mail: journals@sagepub.com; Web site: https://sagepub.com
– Name: PeerReviewed
  Label: Peer Reviewed
  Group: SrcInfo
  Data: Y
– Name: Pages
  Label: Page Count
  Group: Src
  Data: 16
– Name: DatePubCY
  Label: Publication Date
  Group: Date
  Data: 2024
– Name: TypeDocument
  Label: Document Type
  Group: TypDoc
  Data: Journal Articles<br />Reports - Research
– Name: Audience
  Label: Education Level
  Group: Audnce
  Data: <searchLink fieldCode="EL" term="%22Higher+Education%22">Higher Education</searchLink><br /><searchLink fieldCode="EL" term="%22Postsecondary+Education%22">Postsecondary Education</searchLink>
– Name: Subject
  Label: Descriptors
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Artificial+Intelligence%22">Artificial Intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Intention%22">Intention</searchLink><br /><searchLink fieldCode="DE" term="%22Measures+%28Individuals%29%22">Measures (Individuals)</searchLink><br /><searchLink fieldCode="DE" term="%22Development%22">Development</searchLink><br /><searchLink fieldCode="DE" term="%22Validity%22">Validity</searchLink><br /><searchLink fieldCode="DE" term="%22College+Students%22">College Students</searchLink><br /><searchLink fieldCode="DE" term="%22Student+Behavior%22">Student Behavior</searchLink><br /><searchLink fieldCode="DE" term="%22Foreign+Countries%22">Foreign Countries</searchLink><br /><searchLink fieldCode="DE" term="%22Student+Attitudes%22">Student Attitudes</searchLink><br /><searchLink fieldCode="DE" term="%22Universities%22">Universities</searchLink>
– Name: Subject
  Label: Geographic Terms
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22China%22">China</searchLink><br /><searchLink fieldCode="DE" term="%22China+%28Beijing%29%22">China (Beijing)</searchLink><br /><searchLink fieldCode="DE" term="%22China+%28Shanghai%29%22">China (Shanghai)</searchLink><br /><searchLink fieldCode="DE" term="%22China+%28Guangzhou%29%22">China (Guangzhou)</searchLink>
– Name: DOI
  Label: DOI
  Group: ID
  Data: 10.1177/21582440241242188
– Name: ISSN
  Label: ISSN
  Group: ISSN
  Data: 2158-2440
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: As artificial intelligence (AI) permeates almost all aspects of our lives, university students need to acquire relevant knowledge, skills, and attitudes to adapt to the challenges it poses. This study reports the development and validation of a scale called the Artificial Intelligence Learning Intention Scale (AILIS). AILIS was designed to measure the different factors that shape university students' behavioral intentions to learn about AI and their AI learning. We recruited 907 Chinese university students who answered the survey. The scale is comprised of 9 factors that are categorized into various dimensions pertaining to epistemic capacity (AI basic knowledge, programming efficacy, designing AI for social good), facilitating environments (actual use of AI systems, subjective norms, access to support and technology), psychological attitudes (resilience, optimism, personal relevance), and focal outcomes (behavioral intention to learn AI, actual learning of AI). Reliability analyses and confirmatory factor analyses indicated that the scale has acceptable reliability and construct validity. Structural equational modeling results demonstrated the critical role played by epistemic capacity, facilitating environments, and psychological attitudes in promoting students' behavioral intentions and actual learning of AI. Overall, the findings revealed that university students express a strong intention to learn about AI, and this behavioral intention is positively associated with actual learning. The study contextualizes the theory of planned behavior for university AI education, provides guidelines on the design of AI curriculum courses, and proposes a possible tool to evaluate university AI curriculum.
– Name: AbstractInfo
  Label: Abstractor
  Group: Ab
  Data: As Provided
– Name: Note
  Label: Notes
  Group: Note
  Data: https://doi.org/10.6084/m9.figshare.23501316
– Name: DateEntry
  Label: Entry Date
  Group: Date
  Data: 2024
– Name: AN
  Label: Accession Number
  Group: ID
  Data: EJ1433407
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=eric&AN=EJ1433407
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1177/21582440241242188
    Languages:
      – Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 16
    Subjects:
      – SubjectFull: Artificial Intelligence
        Type: general
      – SubjectFull: Intention
        Type: general
      – SubjectFull: Measures (Individuals)
        Type: general
      – SubjectFull: Development
        Type: general
      – SubjectFull: Validity
        Type: general
      – SubjectFull: College Students
        Type: general
      – SubjectFull: Student Behavior
        Type: general
      – SubjectFull: Foreign Countries
        Type: general
      – SubjectFull: Student Attitudes
        Type: general
      – SubjectFull: Universities
        Type: general
      – SubjectFull: China
        Type: general
      – SubjectFull: China (Beijing)
        Type: general
      – SubjectFull: China (Shanghai)
        Type: general
      – SubjectFull: China (Guangzhou)
        Type: general
    Titles:
      – TitleFull: Development and Validation of the Artificial Intelligence Learning Intention Scale (AILIS) for University Students
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Ching Sing Chai
      – PersonEntity:
          Name:
            NameFull: Ding Yu
      – PersonEntity:
          Name:
            NameFull: Ronnel B. King
      – PersonEntity:
          Name:
            NameFull: Ying Zhou
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 04
              Type: published
              Y: 2024
          Identifiers:
            – Type: issn-electronic
              Value: 2158-2440
          Numbering:
            – Type: volume
              Value: 14
            – Type: issue
              Value: 2
          Titles:
            – TitleFull: SAGE Open
              Type: main
ResultId 1