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Cited by 17Student Assessment and FeedbackEducation, Achievement, and GiftednessArtificial Intelligence in Education

The Impact of Generative Artificial Intelligence-based Formative Feedback on the Mathematical Motivation of Chinese Grade 4 Students: a Case Study

Zheng, Wenqi, Cheung Tse, Alex Wing·2023·2023 IEEE International Conference on Teaching, Assessment and Learning for Engineering (TALE)
DOI 10.1109/tale56641.2023.10398319
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Abstract

Formative assessment can be a powerful approach in influencing student mathematical motivation, and the emergence of generative artificial intelligence (AI) offers more possibilities to do so, especially when using feedback for formative assessment. Nonetheless, the literature review of this study found limited development and research on investigating the impact of using generative AI-based formative feedback on student mathematical motivation. Therefore, this study examined the impact of implementing Class Optimization Master, generative AI-based formative feedback software, in promoting student mathematical motivation. The study employed a case study on fourth-grade students in a primary school in China. Semi-structured interviews with 21 students and two teachers were conducted face-to-face. Then, the interview data were analyzed by thematic analysis based on the three-dimensions theoretical framework of Mathematical Motivation Scale: Beliefs, Engagement, and Attitude. This study found that the use of AI-generated formative feedback enhanced student mathematical motivation by (1) boosting confidence, promoting socio-emotional interaction, and raising the importance of mathematics; (2) rewarding and inspiring a preference for mathematics; and (3) stimulating interest and mental-physical effort. These results provide insights for educators to design how to make use of AI-generated formative feedback to promote mathematical learning. It nonetheless calls for more research using quantitative methods and from different perspectives on this topic.

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Crossref · 10.1109/tale56641.2023.10398319

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  • These studies combine individualized AI interactions, timely feedback, and teacher facilitation to raise engagement, satisfaction, and confidence.

    “They respond to well-documented problems in traditional instruction-low motivation, limited participation, and scarce affective support-by combining individualized AI interactions, timely feedback, and teacher facilitation to raise engagement, satisfaction, and confidence [109,110].”

    From A Systematic Review of Generative AI in K–12: Mapping Goals, Activities, Roles, and Outcomes via the 3P Model (2025) · cites this paper as [110]

  • Across writing, reading, and mathematics tasks, students repeatedly submit work to AI systems, receive concrete suggestions, and resubmit revised work, creating a cycle of practice, feedback, and renewed practice.

    “Across writing, reading, and mathematics tasks, students repeatedly submit texts, assignments, or answers to AI systems, receive concrete suggestions on error correction, structure, and language use, and then resubmit revised work, creating a cycle of practice, feedback, and renewed practice [75,77,82,83,87,90,110].”

    From A Systematic Review of Generative AI in K–12: Mapping Goals, Activities, Roles, and Outcomes via the 3P Model (2025) · cites this paper as [110]

  • A large subset of studies casts GenAI as a grader in writing and reading assessments, delivering analytic diagnoses and scores across dimensions such as grammar, structure, and argumentation.

    “A large subset of studies casts GenAI as a grader. During writing and reading assessments, AI delivers analytic diagnoses and scores across dimensions such as grammar, structure, and argumentation; teachers then prune or augment these prompts before returning them to students, who revise line-by-line, after which AI re-checks the latest draft [77,83,110,124,130].”

    From A Systematic Review of Generative AI in K–12: Mapping Goals, Activities, Roles, and Outcomes via the 3P Model (2025) · cites this paper as [110]

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Authors
Zheng, W., & Cheung Tse, A. W.
Affiliations
  • University of Hong Kong (HK)
  • The University of Hong Kong,Facultv of Education,China
ORCID
Zheng, Wenqi
Venue
2023 IEEE International Conference on Teaching, Assessment and Learning for Engineering (TALE)
Publisher
IEEE
Published
2023-11-28
Pages
1-8
DOI
10.1109/tale56641.2023.10398319
OpenAlex
W4391266261
Cited by
13
References
62
Citations by year
2026: 4 · 2025: 7 · 2024: 2
FWCI
30.74
Type
conference paper
Language
en
Open access
closed
Licence
Locations
  • IEEE International Conference on Teaching, Assessment, and Learning for Engineering (publishedVersion)
  • The HKU Scholars Hub (University of Hong Kong) (submittedVersion)
Topics
  • Social Sciences › Education › Student Assessment and Feedback
  • Social Sciences › Psychology › Experimental and Cognitive Psychology › Education, Achievement, and Giftedness
  • Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
Keywords
generative AI, formative feedback, mathematics learning motivation, elementary school students, fourth grade mathematics, student self-confidence, socio-emotional interaction, student attitudes toward mathematics

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