Generative AI Technologies, Techniques & Tensions: A Primer

TL;DR

This chapter explores the nature, components, and societal implications of generative AI, emphasizing the importance of understanding its foundations and human-like behavior for responsible use and further research.

Contribution

It provides a detailed decomposition of generative AI systems into components, highlighting their foundations, affordances, tensions, and implications for educational and behavioral research.

Findings

Generative AI systems are composed of data, models, features, and user inputs.

Their human-like surface behavior influences user expectations and interactions.

Educational research methods are well suited to study and evaluate these systems.

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.