This week, we continued with our group project presentations. We made it through 4 different groups, including my own.
As a continuation from the post about the first presentation, this presentation focused more on some different pedagogical frameworks that best connected with our topic.
While we included a lot on our website, including Self-Regulated Learning, Inquiry-Based Learning, Adaptive & Personalized Learning, Universal Design for Learning (UDL), Personalized Learning, Socratic Questioning with AI, Blended Learning Models (i.e., Station rotation, flipped classrooms, or self-paced structures with chatbot supports), and Adaptive & Personalized Learning.
Of this list, we specifically focused on Self-Regulated Learning, Inquiry-Based Learning, and Adaptive & Personalized Learning.
For each framework, we include a different page on our website, and this is the information that we put together for the three we discussed in our presentation (Ronsky et al., 2025):
Self-Regulated Learning:
Self-regulated learning is a cyclical process where students plan, monitor, and evaluate their learning strategies and outcomes (Zimmerman, 2002).
Three Phases (Zimmerman, 2000):
- Forethought: goal-setting and planning
- Performance: self-control and self-monitoring
- Self-Reflection: self-evaluation and strategy adjustment
Research:
- Zimmerman (2002) found that high SRL correlates with higher achievement across subjects.
- Panadero (2017) emphasizes SRL as critical for 21st-century learning, enabling adaptability, persistence, and autonomy.
- A systematic review by Guan et al.(2024) discovered that educational chatbots have mainly promoted learners to identify learning resources, carry out appropriate learning strategies, and metacognitively track their studying.
- Chang et al. (2023) found that learners need to learn about prompting to help with goal setting and planning, and AI chatbots need to be programmed with reverse prompting to provide monitoring and strong feedback, and that there needs to be a data-driven program that allows the chatbot to provide learning analytics so that learners can reflect on their learning.
Inquiry-Based Learning:
Inquiry-based learning encourages students to explore open-ended questions through research, curiosity, and self-directed learning. When paired with generative AI tools like ChatGPT, this model becomes even more important. AI is not used to provide “final answers” but rather to spark inquiry, scaffold thinking, and support deeper investigation or research.
Rather than replacing original thought, AI becomes a thinking partner, helping students frame questions, consider new angles, and gather diverse perspectives.
As Yeh (2024) emphasizes, integrating AI into inquiry-based learning transforms the classroom from a teacher-centred environment into a student-centred space. Utilizing AI repositions teachers as facilitators.
Adaptive and Personalized Learning:
Personalized Learning: Tailors instruction to individual student preferences, needs, and interests.
Adaptive Learning: Uses data to adjust content, pace, and support in real time.
How AI Chatbots Help:
- Respond to input instantly, identifying gaps or strengths.
- Offer different types of support (hints, explanations, examples) based on user behaviour.
- Adjust difficulty and topic sequencing based on student responses.
Research:
Implementing Learning Principles with a Personal AI Tutor: A Case Study (Baillifard et al., Sep 2023)
- Uses GPT‑3 to generate microlearning questions with spaced repetition and individualized difficulty, leading to up to a 15‑percentile-point improvement in exam scores
Perceptions and Use of AI Chatbots among Students in Higher Education: A Scoping Review (Labadze et al., 2024)
- A meta-analysis showing that AI chatbots with personalized interactivity tend to boost performance, motivation, self-efficacy, and perceived value of learning
Each of these frameworks that we chose to focus on for this presentation plays a big role in how AI can be used in education. With these frameworks, educators can take into account how they want to model a certain lesson or course and determine the best way they can use AI to achieve their ideas.
We then continued on to have a group activity where we split everyone into groups and go through a slide deck where they were tasked with a topic, and they chose a chatbot to go through the activity. They were then told to go to the slide following their prompt and answer some of the questions they were provided. The responses we received were actually pretty interesting, and are included in our groups website which can be accessed using this link: https://aichatbotsinschools.my.canva.site
This presentation allowed us to gather some insight into how our classmates interacted with AI. It also allowed us to dive further into our topic to start to generate some ideas for how we can address our overall issue of classroom engagement and how AI can help us as educators work towards re-engaging our students.
The next presentation will be our closing one in which we will finally address our issue, provide helpful tools and resources, and share our conclusions based on everything we as a group decided. I am excited to finish our website so that we can show what we learned and share the resource with anyone who may find it useful.
References:
Baillifard, G., Diab, E., Huang, A., Liu, J., Gyllstrom, K., & Choudhury, T. (2023, September). Implementing learning principles with a personal AI tutor: A case study. arXiv. https://arxiv.org/abs/2309.00043
Chang, D. H., Lin, M. P.-C., Hajian, S., & Wang, Q. Q. (2023). Educational Design Principles of Using AI Chatbot That Supports Self-Regulated Learning in Education: Goal Setting, Feedback, and Personalization. Sustainability 15(17), 12921. https://doi.org/10.3390/su151712921
Guan, R., Raković, M., Chen, G., & Gašević, D. (2024). How educational chatbots support self-regulated learning? A systematic review of the literature. Education and Information Technologies 30(4), 4493–4518. https://doi.org/10.1007/s10639-024-12881-y
Labadze, L., Toader, D. C., Moise, D., & Dumitru, D. (2024). Perceptions and use of AI chatbots among students in higher education: A scoping review. Computers and Education: Artificial Intelligence 5, 100155. https://doi.org/10.1016/j.caeai.2024.100155
Panadero, E. (2017). A review of self-regulated learning: Six models and four directions for research. Frontiers in Psychology 8, 422. https://doi.org/10.3389/fpsyg.2017.00422
Ronsky, A., Reader, C., Witkowski, B., Pereira da Silva, S., Alvarez, S. A. (2025). Enhancing Engagement and Differentiation through AI Chatbots in Secondary Classrooms. https://aichatbotsinschools.my.canva.site/
Yeh, H.-C. (2024). The synergy of generative AI and inquiry-based learning: Transforming the landscape of English teaching and learning. Interactive Learning Environments. Advance online publication. https://doi.org/10.1080/10494820.2024.2335491
Zimmerman, B. J. (2000). Attaining self-regulation: A social cognitive perspective. In M. Boekaerts, P. R. Pintrich, & M. Zeidner (Eds.), Handbook of self-regulation (pp. 13–39). Academic Press.
Zimmerman, B. J. (2002). Becoming a self-regulated learner: An overview. Theory into Practice 41(2), 64–70. https://doi.org/10.1207/s15430421tip4102_2
