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This study examines the effects of question type and feedback on learning outcomes in a hybrid graduate-level course. By analyzing data from 32 students over 30,198 interactions, we assess the efficacy of unique versus repeated questions and the impact of feedback on student learning. The findings reveal students demonstrate significantly better knowledge generalization when encountering unique questions compared to repeated ones, even though they perform better with repeated opportunities. Moreover, we find that the timing of explanatory feedback is a more robust predictor of learning outcomes than the practice opportunities themselves. These insights suggest that educational practices and technological platforms should prioritize a variety of questions to enhance the learning process. The study also highlights the critical role of feedback; opportunities preceding feedback are less effective in enhancing learning., Comment: To be published in Proceedings of the Eleventh ACM Conference on Learning at Scale (L@S 24), July 18-20, 2024, Atlanta, GA, USA. ACM, New York, NY, USA, 5 pages

The ActiveAI project addresses key challenges in AI education for grades 7-9 students by providing an engaging AI literacy learning experience based on the AI4K12 knowledge framework. Utilizing learning science mechanisms such as goal-based scenarios, immediate feedback, project-based learning, and intelligent agents, the app incorporates a variety of learner inputs like sliders, steppers, and collectors to enhance understanding. In these courses, students work on real-world scenarios like analyzing sentiment in social media comments. This helps them learn to effectively engage with AI systems and develop their ability to evaluate AI-generated output. The Learning Engineering Process (LEP) guided the project's creation and data instrumentation, focusing on design and impact. The project is currently in the implementation stage, leveraging the intelligent tutor design principles for app development. The extended abstract presents the foundational design and development, with further evaluation and research to be conducted in the future.

This paper presents a comprehensive exploration of leveraging Large Language Models (LLMs), specifically GPT-4, in the field of instructional design. With a focus on scaling evidence-based instructional design expertise, our research aims to bridge the gap between theoretical educational studies and practical implementation. We discuss the benefits and limitations of AI-driven content generation, emphasizing the necessity of human oversight in ensuring the quality of educational materials. This work is elucidated through two detailed case studies where we applied GPT-4 in creating complex higher-order assessments and active learning components for different courses. From our experiences, we provide best practices for effectively using LLMs in instructional design tasks, such as utilizing templates, fine-tuning, handling unexpected output, implementing LLM chains, citing references, evaluating output, creating rubrics, grading, and generating distractors. We also share our vision of a future recommendation system, where a customized GPT-4 extracts instructional design principles from educational studies and creates personalized, evidence-supported strategies for users' unique educational contexts. Our research contributes to understanding and optimally harnessing the potential of AI-driven language models in enhancing educational outcomes.

Contextualizing problems to align with student interests can significantly improve learning outcomes. However, this task often presents scalability challenges due to resource and time constraints. Recent advancements in Large Language Models (LLMs) like GPT-4 offer potential solutions to these issues. This study explores the ability of GPT-4 in the contextualization of problems within CTAT, an intelligent tutoring system, aiming to increase student engagement and enhance learning outcomes. Through iterative prompt engineering, we achieved meaningful contextualization that preserved the difficulty and original intent of the problem, thereby not altering values or overcomplicating the questions. While our research highlights the potential of LLMs in educational settings, we acknowledge current limitations, particularly with geometry problems, and emphasize the need for ongoing evaluation and research. Future work includes systematic studies to measure the impact of this tool on students' learning outcomes and enhancements to handle a broader range of problems.