Large Language Agents in Education and Knowledge Work

This paper introduces “Interactionalism,” a framework for learning design in the GenAI era, emphasizing interactional intelligence—meta-cognitive and meta-emotional skills critical for working with AI agents. It proposes actionable pedagogical strategies to foster human-AI collaboration in skill development.

This study evaluates GPT-4’s effectiveness in financial statement analysis, demonstrating superior directional earnings predictions compared to human analysts, especially in challenging scenarios. GPT-4 matches state-of-the-art ML models in accuracy and generates valuable narrative insights. Trading strategies based on GPT-4 yield higher Sharpe ratios and alphas, indicating its potential for central roles in financial analysis and

This paper advocates for developing AI systems as collaborative “thought partners” that engage in shared reasoning, complement human cognition, and build human-compatible models. It proposes a Bayesian framework for designing such systems, emphasizing reliability, trust, and mutual understanding in human-AI partnerships.

This paper explores the transformative potential of generative AI in human learning, highlighting its ability to personalize education, diversify resources, and enhance assessments. Key challenges include ethical concerns, model limitations, and traditional assessment disruption. Rigorous research and AI literacy are essential for effective integration.

This study, conducted with Boston Consulting Group, investigates AI’s impact on knowledge-intensive tasks using GPT-4. Consultants with AI access completed tasks 25% faster and produced 40% higher quality outcomes. Two successful AI integration patterns emerged: “Centaurs,” who selectively delegated tasks to AI, and “Cyborgs,” who fully integrated AI into their workflow. However, for tasks beyond