🤖 AI Summary
Users often treat generative AI as a black box, leading to cognitive biases and misuse. This work proposes an integrative conceptual framework that deconstructs generative AI into interacting components—data, model architecture, product functionality, and user input—and situates them within the historical evolution of computational paradigms. By synthesizing insights from statistical learning theory, anthropomorphic behavioral characteristics, large language model architectures, human–AI interaction modeling, and educational research methodologies, the framework underscores the distinctive role of educational scholars in uncovering latent mechanisms, addressing epistemic uncertainty, and articulating human–AI collaboration dynamics. It offers a coherent conceptual map to support more rigorous experimental design, critical interpretation of AI behaviors, and responsible deployment in educational contexts.
📝 Abstract
Generative AI systems have entered everyday academic, professional, and personal life with remarkable speed, yet most users encounter them as mysterious artifacts rather than intelligible systems. This chapter discusses large language models within a broader historical shift in computing paradigms and argues that many of the confusions surrounding their use arise from a mismatch between how these systems are built, how they behave, and how people expect computers to behave writ large. Rather than treating generative AI as a monolithic technology, the chapter decomposes it into interacting components, spanning data, models, product features, and user inputs, each introducing distinct affordances and tensions. Particular attention is given to the statistical and data-based foundations of these systems and to the fact that their surface behavior is explicitly human-like, a combination that places them squarely within the intellectual traditions of educational and behavioral research. From this perspective, educational researchers are unusually well positioned to study, evaluate, and productively use generative AI systems, drawing on established methods for modeling latent processes, managing uncertainty, and interpreting complex human-system interactions. The goal is to equip readers with a conceptual map that supports more informed experimentation, critical interpretation, and responsible use as these systems continue to evolve.