Large Language Models (LLMs) are now increasingly widely used to simulate personas in virtual environments, leveraging their instruction-following capability. However, we discovered that even state-of-the-art LLMs cannot simulate personas with reversed performance (e.g., student personas with low proficiency in educational … Continued
As researchers rush to investigate the potential of AI tools like ChatGPT to enhance learning, a number of well-known and well-documented pitfalls threaten the validity of this emerging research. Issues of media comparison research, where the confounding of instructional methods … Continued
Machine learning (ML) systems are vulnerable to performance decline over time due to dataset shift. To address this problem, experts often suggest that ML systems should be regularly updated to ensure ongoing performance stability. Some scholarly literature has begun to … Continued
The rise of Generative AI, and Large Language Models (LLMs) in particular, is fundamentally changing cognitive processes in knowledge work, raising critical questions about their impact on human reasoning and problem-solving capabilities. As these AI systems become increasingly integrated into … Continued
Heather Jorgenson recently present at the Society for Information Technology and Teacher Education (SITTE) conference in Orlando. Title: “Professional Learning Leaders’ Perspectives on How Automated and Personalized Feedback Reports Can Support Teacher Learning” Authors: Heather Jorgenson, Jamie Mikeska, and Dante … Continued
The rapid growth of artificial intelligence (AI) technologies has changed decision-making in many fields. But, it has also raised major privacy and ethical concerns. However, many AI incidents taxonomies and guidelines for academia, industry, and government lack grounding in real-world … Continued
Generative AI has the potential to transform personalization and accessibility of education. However, it raises serious concerns about accuracy and helping students become independent critical thinkers. In this study, we designed a helpful AI “Peer” to help students correct fundamental … Continued
Large language models (LLMs) are being increasingly incorporated into scientific workflows. However, we have yet to fully grasp the implications of this integration. How should the advancement of large language models affect the practice of science? For this opinion piece, … Continued
Abstract As Generative AI (GenAI) capabilities expand, understanding how to preserve and develop human expertise while leveraging AI’s benefits becomes increasingly critical. Through empirical studies in two contexts — survey article authoring in scholarly research and business document sensemaking — … Continued
Reasoning-enhanced large language models (LLMs) explicitly generate intermediate reasoning steps prior to generating final answers, helping the model excel in complex problem-solving. In this paper, we demonstrate that this emerging generation framework offers a unique opportunity for more fine-grained control … Continued