How AI‐Generated Feedback Hinders or Helps Learning: A Heterogeneous TNA Study of Learning Dynamics.
Background: Despite growing integration of generative AI in educational settings, little is known about whether and to what extent AI‐generated real‐time feedback can support first‐grade students in solving multimodal mathematical tasks. Objectives: Our study aims to examine student–AI feedback dyna...
| Publicado en: | Journal of Computer Assisted Learning Vol. 42; no. 4; pp. 1 - 18 |
|---|---|
| Autores principales: | , , |
| Formato: | pictorial research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
Aug2026
|
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=195655213&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 195655213 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 02664909 6M1 jtl: Journal of Computer Assisted Learning issn: 02664909 maglogo: Y pubinfo: dt: Aug2026 vid: 42 iid: 4 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 195655213 195655213 195655213 10.1002/jcal.70285 195655213 ppf: 1 ppct: 17 formats: tig: atl: How AI‐Generated Feedback Hinders or Helps Learning: A Heterogeneous TNA Study of Learning Dynamics. aug: au: López‐Pernas, Sonsoles Misiejuk, Kamila Saqr, Mohammed affil: School of Computing, University of Eastern Finland, Joensuu, Finland sug: subj: Artificial Intelligence, Generative Feedback User-Computer Interface Evaluation Task Performance and Analysis Mathematics Students, Elementary Human Learning Data Analysis Software Chi Square Test Probability Regression Funding Source ab: Background: Despite growing integration of generative AI in educational settings, little is known about whether and to what extent AI‐generated real‐time feedback can support first‐grade students in solving multimodal mathematical tasks. Objectives: Our study aims to examine student–AI feedback dynamics from a process‐oriented perspective, focusing on how students interact with AI feedback after incorrectly solving multimodal mathematical tasks and which interaction patterns lead to successful re‐attempts. Methods: We analyze data from 13,000 first‐grade pupils across two countries who completed a single‐session assessment of 16 foundational numeracy skills via multimodal tasks, where incorrect and correct responses triggered real‐time feedback from a GPT‐4.1‐based system. All feedback instances were collected and qualitatively coded to investigate how student‐AI interaction sequences differ between successful and unsuccessful re‐attempts, and to what extent specific actions or interaction patterns predict successful attempts. Data were examined using Heterogeneous Transition Network Analysis and sequential pattern mining. Moreover, chi‐squared tests and regression models were used to test the association between successful and unsuccessful attempts and interaction types and sequences thereof. Results: Findings revealed that feedback effectiveness depended not on type alone but on how it was embedded within broader interaction sequences. Successful re‐attempts were more likely to follow clarification‐oriented feedback, whereas unsuccessful ones were more commonly preceded by question‐based prompts or direct orders, which reflect more highly anthropomorphic and authoritative forms of address. The absence of AI feedback was associated with poorer recovery. Conclusions: These findings are situated within the broader concern that misaligned generative AI scaffolding and feedback may deepen confusion or reinforce misconceptions instead of supporting learning. Lay Summary: What is currently known about this topic? ○Generative AI can provide instant, personalized feedback in math○Most studies focus on test scores, not learning processes○Multimodal tasks increase cognitive demands for young learners.○Little is known about AI feedback for early numeracy skills.What does this paper add? ○We analyze 13,000 pupils' real‐time interactions with AI feedback○We study how feedback sequences relate to success or failure.○Clarifying feedback supports more successful retries○Feedback sequencing matters more than type.Implications for practice or policy ○Design AI feedback to clarify concepts before prompting action○Avoid overreliance on directive or question‐only feedback○Any type of (AI) feedback is better than none pubtype: Academic Journal doctype: pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|