Gautam Yadav

Ying Jui Tseng, Gautam Yadav

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.

Gautam Yadav

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.