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With increasing interest in computer‐assisted educa‐ tion, AI‐integrated systems become highly applicable with their ability to adapt based on user interactions. In this context, this paper focuses on understanding and analysing first‐year undergraduate student responses to an intelligent educational system that applies multi‐agent reinforcement learning as an AI tutor. With human–computer interaction at the centre, we discuss principles of interface design and educational gamification in the context of multiple years of student observations, student feedback surveys and focus group interviews. We show positive feedback from the design methodology we discuss as well as the overall process of providing automated tutoring in a gamified virtual environment. We also discuss students' thinking in the context of gamified educational systems, as well as unexpected issues that may arise when implementing such systems. Ultimately, our design iterations and analysis both offer new insights for practical implementation of computer‐assisted educational systems, focusing on how AI can augment, rather than replace, human intelligence in the classroom. Practitioner notesWhat is already known about this topicAI‐integrated systems show promise for personalizing learning and improving student education.Existing research has shown the value of personalized learner feedback.Engaged students learn more effectively.What this paper addsStudent opinions of and responses to an HCI‐based personalized educational system.New insights for practical implementation of AI‐integrated educational systems informed by years of student observations and system improvements.Qualitative insights into system design to improve human–computer interaction in educational systems.Implications for practice and/or policyActionable design principles for computer‐assisted tutoring systems derived from first‐hand student feedback and observations.Encourage new directions for human–computer interaction in educational systems.
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Providing tailored reflection instructions in collaborative learning using large language models
AbstractThe relative effectiveness of reflection either through student generation of contrasting cases or through provided contrasting cases is not well‐established for adult learners. This paper presents a classroom study to investigate this comparison in a college level Computer Science (CS) course where groups of students worked collaboratively to design database access strategies. Forty‐four teams were randomly assigned to three reflection conditions ([GEN] directive to generate a contrasting case to the student solution and evaluate their trade‐offs in light of the principle, [CONT] directive to compare the student solution with a provided contrasting case and evaluate their trade‐offs in light of a principle, and [NSI] a control condition with a non‐specific directive for reflection evaluating the student solution in light of a principle). In the CONT condition, as an illustration of the use of LLMs to exemplify knowledge transformation beyond knowledge construction in the generation of an automated contribution to a collaborative learning discussion, an LLM generated a contrasting case to a group's solution to exemplify application of an alternative problem solving strategy in a way that highlighted the contrast by keeping many concrete details the same as those the group had most recently collaboratively constructed. While there was no main effect of condition on learning based on a content test, low‐pretest student learned more from CONT than GEN, with NSI not distinguishable from the other two, while high‐pretest students learned marginally more from the GEN condition than the CONT condition, with NSI not distinguishable from the other two. Practitioner notesWhat is already known about this topicReflection during or even in place of computer programming is beneficial for learning of principles for advanced computer science when the principles are new to students.Generation of contrasting cases and comparing contrasting cases have both been demonstrated to be effective as opportunities to learn from reflection in some contexts, though questions remain about ideal applicability conditions for adult learners.Intelligent conversational agents can be used effectively to deliver stimuli for reflection during collaborative learning, though room for improvement remains, which provides an opportunity to demonstrate the potential positive contribution of large language models (LLMs).What this paper addsThe study contributes new knowledge related to the differences in applicability conditions between generation of contrasting cases and comparison across provided contrasting cases for adult learning.The paper presents an application of LLMs as a tool to provide contrasting cases tailored to the details of actual student solutions.The study provides evidence from a classroom intervention study for positive impact on student learning of an LLM‐enabled intervention.Implications for practice and/or policyAdvanced computer science curricula should make substantial room for reflection alongside problem solving.Instructors should provide reflection opportunities for students tailored to their level of prior knowledge.Instructors would benefit from training to use LLMs as tools for providing effective contrasting cases, especially for low‐prior‐knowledge students.
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- Award ID(s):
- 2222762
- PAR ID:
- 10644940
- Publisher / Repository:
- Wiley-Blackwell
- Date Published:
- Journal Name:
- British Journal of Educational Technology
- Volume:
- 56
- Issue:
- 2
- ISSN:
- 0007-1013
- Format(s):
- Medium: X Size: p. 531-550
- Size(s):
- p. 531-550
- Sponsoring Org:
- National Science Foundation
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