View in EDS

When Generative AI Meets Socratic Method: Investigating Programming Learning Dynamics through Behaviours, Interaction Qualities and Perceptions

Saved in:
Bibliographic Details
Title: When Generative AI Meets Socratic Method: Investigating Programming Learning Dynamics through Behaviours, Interaction Qualities and Perceptions
Language: English
Authors: Dan Sun, Yi Zheng, Jie Xu (ORCID 0000-0003-3345-4116), Zhanshan Yang
Source: Journal of Computer Assisted Learning. 2026 42(2).
Availability: Wiley. Available from: John Wiley & Sons, Inc. 111 River Street, Hoboken, NJ 07030. Tel: 800-835-6770; e-mail: cs-journals@wiley.com; Web site: https://www.wiley.com/en-us
Peer Reviewed: Y
Page Count: 17
Publication Date: 2026
Document Type: Journal Articles
Reports - Research
Education Level: Higher Education
Postsecondary Education
Descriptors: College Students, Programming, Troubleshooting, Questioning Techniques, Artificial Intelligence, Technology Uses in Education, Critical Thinking, Problem Solving, Learning Analytics, Epistemology, Network Analysis, Interaction, Feedback (Response), Scaffolding (Teaching Technique), Computer Assisted Instruction, Prompting
DOI: 10.1002/jcal.70210
ISSN: 0266-4909
1365-2729
Abstract: Background: The integration of generative artificial intelligence (GAI) tools like GPT into programming education offers transformative potential through personalised guidance and instant feedback, yet risks fostering overreliance and superficial learning due to their tendency to deliver direct, context-free answers. Objectives: This quasi-experimental study addresses this gap by proposing a Socratic questioning framework to optimise GAI-facilitated programming instruction, emphasising critical thinking over passive solution retrieval. Methods: We compared two pedagogical approaches--GAI-Scaffolded Learning (GSL), where GPT employs structured Socratic dialogue to guide problem-solving and GAI-Direct Learning (GDL), which provides immediate answers without guided inquiry. This research collected learners' programming behaviours, interactions data with GPT from screen recordings and platform log data and perceptions data. This research further utilised multiple learning analytics approaches (i.e., click stream analysis, lag-sequential analysis, epistemic network analysis [ENA] and statistics) to compare learners' programming behaviours, interaction patterns and perceptions under two approaches. Results and Conclusions: Through an analysis of 80 college students' programming behaviours, interaction qualities and perceptions, we found some intriguing results. First, GSL engaged in cyclical, reflective practices (debugging, Socratic questioning, console use), while GDL prioritised rapid fixes via trial-and-error with GPT code, risking superficial mimicry and over-reliance on external resources. Second, ENA highlighted GSL's deeper engagement through interconnected feedback, emotional support and iterative inquiry, reducing frustration and sustaining persistence and GDL interactions focused on surface-level queries, lacking scaffolding for emotional/heuristic integration. Third, GSL maintained positive attitudes due to structured prompts aligning expectations and easing cognitive load. GDL attitudes declined from mismatched expectations and frustration. Implications: Based on these findings, the study proposes pedagogical and developmental implications for future design and development of AI-augmented curricula, providing actionable insights for educators seeking to harness GAI's potential while nurturing critical thinking in programming education.
Abstractor: As Provided
Entry Date: 2026
Accession Number: EJ1500508
Database: ERIC
FullText Text:
  Availability: 0
Header DbId: eric
DbLabel: ERIC
An: EJ1500508
AccessLevel: 3
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: When Generative AI Meets Socratic Method: Investigating Programming Learning Dynamics through Behaviours, Interaction Qualities and Perceptions
– Name: Language
  Label: Language
  Group: Lang
  Data: English
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Dan+Sun%22">Dan Sun</searchLink><br /><searchLink fieldCode="AR" term="%22Yi+Zheng%22">Yi Zheng</searchLink><br /><searchLink fieldCode="AR" term="%22Jie+Xu%22">Jie Xu</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0003-3345-4116">0000-0003-3345-4116</externalLink>)<br /><searchLink fieldCode="AR" term="%22Zhanshan+Yang%22">Zhanshan Yang</searchLink>
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="SO" term="%22Journal+of+Computer+Assisted+Learning%22"><i>Journal of Computer Assisted Learning</i></searchLink>. 2026 42(2).
– Name: Avail
  Label: Availability
  Group: Avail
  Data: Wiley. Available from: John Wiley & Sons, Inc. 111 River Street, Hoboken, NJ 07030. Tel: 800-835-6770; e-mail: cs-journals@wiley.com; Web site: https://www.wiley.com/en-us
– Name: PeerReviewed
  Label: Peer Reviewed
  Group: SrcInfo
  Data: Y
– Name: Pages
  Label: Page Count
  Group: Src
  Data: 17
– Name: DatePubCY
  Label: Publication Date
  Group: Date
  Data: 2026
– 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="%22College+Students%22">College Students</searchLink><br /><searchLink fieldCode="DE" term="%22Programming%22">Programming</searchLink><br /><searchLink fieldCode="DE" term="%22Troubleshooting%22">Troubleshooting</searchLink><br /><searchLink fieldCode="DE" term="%22Questioning+Techniques%22">Questioning Techniques</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+Intelligence%22">Artificial Intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Technology+Uses+in+Education%22">Technology Uses in Education</searchLink><br /><searchLink fieldCode="DE" term="%22Critical+Thinking%22">Critical Thinking</searchLink><br /><searchLink fieldCode="DE" term="%22Problem+Solving%22">Problem Solving</searchLink><br /><searchLink fieldCode="DE" term="%22Learning+Analytics%22">Learning Analytics</searchLink><br /><searchLink fieldCode="DE" term="%22Epistemology%22">Epistemology</searchLink><br /><searchLink fieldCode="DE" term="%22Network+Analysis%22">Network Analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Interaction%22">Interaction</searchLink><br /><searchLink fieldCode="DE" term="%22Feedback+%28Response%29%22">Feedback (Response)</searchLink><br /><searchLink fieldCode="DE" term="%22Scaffolding+%28Teaching+Technique%29%22">Scaffolding (Teaching Technique)</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+Assisted+Instruction%22">Computer Assisted Instruction</searchLink><br /><searchLink fieldCode="DE" term="%22Prompting%22">Prompting</searchLink>
– Name: DOI
  Label: DOI
  Group: ID
  Data: 10.1002/jcal.70210
– Name: ISSN
  Label: ISSN
  Group: ISSN
  Data: 0266-4909<br />1365-2729
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Background: The integration of generative artificial intelligence (GAI) tools like GPT into programming education offers transformative potential through personalised guidance and instant feedback, yet risks fostering overreliance and superficial learning due to their tendency to deliver direct, context-free answers. Objectives: This quasi-experimental study addresses this gap by proposing a Socratic questioning framework to optimise GAI-facilitated programming instruction, emphasising critical thinking over passive solution retrieval. Methods: We compared two pedagogical approaches--GAI-Scaffolded Learning (GSL), where GPT employs structured Socratic dialogue to guide problem-solving and GAI-Direct Learning (GDL), which provides immediate answers without guided inquiry. This research collected learners' programming behaviours, interactions data with GPT from screen recordings and platform log data and perceptions data. This research further utilised multiple learning analytics approaches (i.e., click stream analysis, lag-sequential analysis, epistemic network analysis [ENA] and statistics) to compare learners' programming behaviours, interaction patterns and perceptions under two approaches. Results and Conclusions: Through an analysis of 80 college students' programming behaviours, interaction qualities and perceptions, we found some intriguing results. First, GSL engaged in cyclical, reflective practices (debugging, Socratic questioning, console use), while GDL prioritised rapid fixes via trial-and-error with GPT code, risking superficial mimicry and over-reliance on external resources. Second, ENA highlighted GSL's deeper engagement through interconnected feedback, emotional support and iterative inquiry, reducing frustration and sustaining persistence and GDL interactions focused on surface-level queries, lacking scaffolding for emotional/heuristic integration. Third, GSL maintained positive attitudes due to structured prompts aligning expectations and easing cognitive load. GDL attitudes declined from mismatched expectations and frustration. Implications: Based on these findings, the study proposes pedagogical and developmental implications for future design and development of AI-augmented curricula, providing actionable insights for educators seeking to harness GAI's potential while nurturing critical thinking in programming education.
– Name: AbstractInfo
  Label: Abstractor
  Group: Ab
  Data: As Provided
– Name: DateEntry
  Label: Entry Date
  Group: Date
  Data: 2026
– Name: AN
  Label: Accession Number
  Group: ID
  Data: EJ1500508
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=eric&AN=EJ1500508
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1002/jcal.70210
    Languages:
      – Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 17
    Subjects:
      – SubjectFull: College Students
        Type: general
      – SubjectFull: Programming
        Type: general
      – SubjectFull: Troubleshooting
        Type: general
      – SubjectFull: Questioning Techniques
        Type: general
      – SubjectFull: Artificial Intelligence
        Type: general
      – SubjectFull: Technology Uses in Education
        Type: general
      – SubjectFull: Critical Thinking
        Type: general
      – SubjectFull: Problem Solving
        Type: general
      – SubjectFull: Learning Analytics
        Type: general
      – SubjectFull: Epistemology
        Type: general
      – SubjectFull: Network Analysis
        Type: general
      – SubjectFull: Interaction
        Type: general
      – SubjectFull: Feedback (Response)
        Type: general
      – SubjectFull: Scaffolding (Teaching Technique)
        Type: general
      – SubjectFull: Computer Assisted Instruction
        Type: general
      – SubjectFull: Prompting
        Type: general
    Titles:
      – TitleFull: When Generative AI Meets Socratic Method: Investigating Programming Learning Dynamics through Behaviours, Interaction Qualities and Perceptions
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Dan Sun
      – PersonEntity:
          Name:
            NameFull: Yi Zheng
      – PersonEntity:
          Name:
            NameFull: Jie Xu
      – PersonEntity:
          Name:
            NameFull: Zhanshan Yang
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 04
              Type: published
              Y: 2026
          Identifiers:
            – Type: issn-print
              Value: 0266-4909
            – Type: issn-electronic
              Value: 1365-2729
          Numbering:
            – Type: volume
              Value: 42
            – Type: issue
              Value: 2
          Titles:
            – TitleFull: Journal of Computer Assisted Learning
              Type: main
ResultId 1