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Educational Design Principles of Using AI Chatbot That Supports Self-Regulated Learning in Education: Goal Setting, Feedback, and Personalization

Author

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  • Daniel H. Chang

    (Faculty of Education, Simon Fraser University, Burnaby, BC V5A 1S6, Canada)

  • Michael Pin-Chuan Lin

    (Faculty of Education, Mount Saint Vincent University, Halifax, NS B3M 2J6, Canada)

  • Shiva Hajian

    (Faculty of Psychology, Kwantlen Polytechnic University, Surrey, BC V3W 2M8, Canada)

  • Quincy Q. Wang

    (Faculty of Education, Simon Fraser University, Burnaby, BC V5A 1S6, Canada)

Abstract

The invention of ChatGPT and generative AI technologies presents educators with significant challenges, as concerns arise regarding students potentially exploiting these tools unethically, misrepresenting their work, or gaining academic merits without active participation in the learning process. To effectively navigate this shift, it is crucial to embrace AI as a contemporary educational trend and establish pedagogical principles for properly utilizing emerging technologies like ChatGPT to promote self-regulation. Rather than suppressing AI-driven tools, educators should foster collaborations among stakeholders, including educators, instructional designers, AI researchers, and developers. This paper proposes three key pedagogical principles for integrating AI chatbots in classrooms, informed by Zimmerman’s Self-Regulated Learning (SRL) framework and Judgment of Learning (JOL). We argue that the current conceptualization of AI chatbots in education is inadequate, so we advocate for the incorporation of goal setting (prompting), self-assessment and feedback, and personalization as three essential educational principles. First, we propose that teaching prompting is important for developing students’ SRL. Second, configuring reverse prompting in the AI chatbot’s capability will help to guide students’ SRL and monitoring for understanding. Third, developing a data-driven mechanism that enables an AI chatbot to provide learning analytics helps learners to reflect on learning and develop SRL strategies. By bringing in Zimmerman’s SRL framework with JOL, we aim to provide educators with guidelines for implementing AI in teaching and learning contexts, with a focus on promoting students’ self-regulation in higher education through AI-assisted pedagogy and instructional design.

Suggested Citation

  • Daniel H. Chang & Michael Pin-Chuan Lin & Shiva Hajian & Quincy Q. Wang, 2023. "Educational Design Principles of Using AI Chatbot That Supports Self-Regulated Learning in Education: Goal Setting, Feedback, and Personalization," Sustainability, MDPI, vol. 15(17), pages 1-15, August.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:17:p:12921-:d:1226247
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    References listed on IDEAS

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    1. Hainora Hamzah & Mohd Isa Hamzah & Hafizhah Zulkifli, 2022. "Systematic Literature Review on the Elements of Metacognition-Based Higher Order Thinking Skills (HOTS) Teaching and Learning Modules," Sustainability, MDPI, vol. 14(2), pages 1-15, January.
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    1. repec:bjc:journl:v:12:y:2025:i:11:p:2195-2203 is not listed on IDEAS
    2. Norah Muflih Alruwaili & Zaiba Ali & Mohd Shuaib Siddiqui & Asad Hassan Butt & Hassan Ahmad & Rahila Ali & Shaden Hamad Alsalem, 2025. "Exploring the Impact of Female Student’s Digital Intelligence on Sustainable Learning and Digital Mental Well-Being: A Case Study of Saudi Arabia," Sustainability, MDPI, vol. 17(14), pages 1-19, July.
    3. Cheong Yew Ooi & Ali Vafaei-Zadeh, 2025. "Integrating Technology Acceptance Model and Task-Technology Fit Theory: Investigating Students’ Intention to Use AI Chatbots in Public Universities," Information Management and Business Review, AMH International, vol. 17(3), pages 184-194.
    4. Tuğba Aydın Yıldız, 2025. "Sustainability Education in L2 Writing: AI-Based Multimodal Awareness and Engagement," Sustainability, MDPI, vol. 17(21), pages 1-30, October.
    5. Michael Pin-Chuan Lin & Arita Li Liu & Eric Poitras & Maiga Chang & Daniel H. Chang, 2024. "An Exploratory Study on the Efficacy and Inclusivity of AI Technologies in Diverse Learning Environments," Sustainability, MDPI, vol. 16(20), pages 1-19, October.

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