AI Assisted Learning

AI Tutoring Outperforms Active Learning 

Gregory Kestin, Kelly Miller, Anna Klales, Timothy Milbourne, Gregorio Ponti (June 2024 - Preprint)

Department of Physics, School of Engineering and Applied Sciences, Harvard University, Cambridge, MA 02138

Abstract

Advances in generative artificial intelligence (GAI) show great potential for improving education. Yet little is known about how this new technology should be used and how effective it can be. Here we report a randomized, controlled study measuring college students’ learning and their perceptions when content is presented through an AI-powered tutor compared with an active learning class. The AI tutor was developed with the same pedagogical best practices as the lectures. We find that students learn more than twice as much in less time when using an AI tutor, compared with the active learning class. They also feel more engaged and more motivated. These findings offer empirical evidence for the efficacy of a widely accessible AI-powered pedagogy in significantly enhancing learning outcomes, presenting a compelling case for its broad adoption in learning environments.

Looking beyond the hype_ AI won’t transform classrooms, but it will help transform education.pdf

Thomas Arnett. (June 17, 2024)

Christensen Institute 

Instructors as Innovators: a Future-focused Approach to New AI Learning Opportunities, With Prompts

Ethan R. Mollick, Lilach Mollick, (April 2024)

University of Pennsylvania - Wharton School

Abstract

This paper explores how instructors can leverage generative AI to create personalized learning experiences for students that transform teaching and learning. We present a range of AI-based exercises that enable novel forms of practice and application including simulations, mentoring, coaching, and co-creation. For each type of exercise, we provide prompts that instructors can customize, along with guidance on classroom implementation, assessment, and risks to consider. We also provide blueprints, prompts that help instructors create their own original prompts. Instructors can leverage their content and pedagogical expertise to design these experiences, putting them in the role of builders and innovators. We argue that this instructor-driven approach has the potential to democratize the development of educational technology by enabling individual instructors to create AI exercises and tools tailored to their students' needs. While the exercises in this paper are a starting point, not a definitive solutions, they demonstrate AI's potential to expand what is possible in teaching and learning.

Towards Responsible Development of Generative AI for Education: An Evaluation-Driven Approach

Irina Jurenka, Markus Kunesch, et al (2024-05-14)

Google DeepMind, Google Research, Google, Google Creative Lab, Arizona State University, Lund University, University of Oxford

Summary by Claire Zau GSV Ventures "On Building AI Models for Education: Google's LearnLM, Khan Academy/MSFT's Phi-3 Models, and OpenAI's ChatGPT Edu" (2024-05-14)

Abstract

A major challenge facing the world is the provision of equitable and universal access to quality education. Recent advances in generative AI (gen AI) have created excitement about the potential of new technologies to offer a personal tutor for every learner and a teaching assistant for every teacher. The full extent of this dream, however, has not yet materialised. We argue that this is primarily due to the difficulties with verbalising pedagogical intuitions into gen AI prompts and the lack of good evaluation practices, reinforced by the challenges in defining excellent pedagogy. Here we present our work collaborating with learners and educators to translate high level principles from learning science into a pragmatic set of seven diverse educational benchmarks, spanning quantitative, qualitative, automatic and human evaluations; and to develop a new set of fine-tuning datasets to improve the pedagogical capabilities of Gemini, introducing LearnLM-Tutor. Our evaluations show that LearnLM-Tutor is consistently preferred over a prompt tuned Gemini by educators and learners on a number of pedagogical dimensions. We hope that this work can serve as a first step towards developing a comprehensive educational evaluation framework, and that this can enable rapid progress within the AI and EdTech communities towards maximising the positive impact of gen AI in education.

AI AGENTS AND EDUCATION: SIMULATED PRACTICE AT SCALE 

Ethan R. Mollick, et al (2024-06-26)

University of Pennsylvania - Wharton School

Abstract

This paper explores the potential of generative AI in creating adaptive educational simulations. By leveraging a system of multiple AI agents, simulations can provide personalized learning experiences, offering students the opportunity to practice skills in scenarios with AI-generated mentors, role-players, and instructor-facing evaluators. We describe a prototype, PitchQuest, a venture capital pitching simulator that showcases the capabilities of AI in delivering instruction, facilitating practice, and providing tailored feedback. The paper discusses the pedagogy behind the simulation, the technology powering it, and the ethical considerations in using AI for education. While acknowledging the limitations and need for rigorous testing, we propose that generative AI can significantly lower the barriers to creating effective, engaging simulations, opening up new possibilities for experiential learning at scale.