Using Real-Time Process Data of Domain-Specific Learning Processes to Provide Adaptive Support for Learning and Instruction: Challenges and Opportunities

Advances in learning technologies now enable continuous real-time analysis of learner process data. While these innovations have generated interest in adaptive educational technologies, most research mistakenly assumes that the processes and indicators involved are largely domain-general. This overlooks how domain-specific cognitive structures and practices crucially shape learning and the meaning of process data. This paper synthesizes current theory and research regarding real-time process data and adaptive support, emphasizing the urgent need for domain-sensitive approaches. I argue that each domain sets unique learning goals, expertise trajectories, and measurement constraints, fundamentally affecting what adaptivity can achieve. I identify key conceptual, methodological, and analytical challenges. Drawing on Strohmaier et al. (2025), the lead article, and other contributions in this special issue of Learning and Instruction, I highlight opportunities and challenges in current approaches. I also outline directions for developing interpretable, instructionally aligned adaptive educational technologies.