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.

This exploratory research conducted a thematic analysis of students’ experiences and utilization of AI tools by students in educational settings. We surveyed 87 undergraduates from two different educational courses at a comprehensive university in Western Canada. Nine integral themes that represent AI’s role in student learning and key issues with respect to AI have been identified. The study yielded three critical insights: the potential of AI to expand educational access for a diverse student body, the necessity for robust ethical frameworks to govern AI, and the benefits of personalized AI-driven support. Based on the results, a model is proposed along with recommendations for an optimal learning environment, where AI facilitates meaningful learning. We argue that integrating AI tools into learning has the potential to promote inclusivity and accessibility by making learning more accessible to diverse students. We also advocate for a shift in perception among educational stakeholders towards AI, calling for de-stigmatization of its use in education. Overall, our findings suggest that academic institutions should establish clear, empirical guidelines defining student conduct with respect to what is considered appropriate AI use.

A chatbot is artificial intelligence software that converses with a user in natural language. It can be instrumental in mitigating teaching workloads by coaching or answering student inquiries. To understand student-chatbot interactions, this study is engineered to optimize student learning experience and instructional design. In this study, we developed a chatbot that supplemented disciplinary writing instructions to enhance peer reviewer’s feedback on draft essays. With 23 participants from a lower-division post-secondary education course, we delved into characteristics of student-chatbot interactions. Our analysis revealed students were often overconfident about their learning and comprehension. Drawing on these findings, we propose a new methodology to identify where improvements can be made in conversation patterns in educational chatbots. These guidelines include analyzing interaction pattern logs to progressively redesign chatbot scripts that improve discussions and optimize learning. We describe new methodology providing valuable insights for designing more effective instructional chatbots by enhancing and engaging student learning experiences through improved peer feedback.

Many research studies imply that argument mapping can enhance writers' abilities in argumentation and critical thinking; however, we need to consolidate the available evidence as the first step to better understand the scope and coverage of these tools. Therefore, this paper adopts a scoping review methodology guided by the PRISMA-ScR framework. Through this systematic scoping approach, we aim to map the breadth of research on argument visualization and learning by identifying key themes and fields and highlighting gaps in empirical studies. By synthesizing existing evidence across disciplines and contexts, we hope that the scoping review will provide a foundation for future research directions, particularly in the areas of experimental validation and pedagogical design. This consolidation is especially timely given the rapid expansion of online and hybrid learning environments where such tools could provide crucial cognitive scaffolding.