Addressing the Reality Gap: A Three-Tension Framework for Agentic AI Adoption
Jason Fournier (Imagine Learning), Kacper {\L}odzikowski (Adam Mickiewicz University, Pozna\'n, Poland)

TL;DR
This paper introduces a three-tension framework to guide the responsible integration of agentic AI systems in education, balancing feasibility, adaptation speed, and mission alignment.
Contribution
It proposes a novel framework for evaluating and designing AI in education, addressing practical, temporal, and ethical challenges.
Findings
Illustrates tensions with early evidence from education sectors
Provides a practical framework for decision-makers
Identifies emerging trends and open research directions
Abstract
Generative AI has rapidly entered education through free consumer tools, outpacing the ability of schools and universities to respond. Now a new wave of more autonomous agentic AI systems--with the capacity to plan and act towards goals--promises both greater educational personalization and greater disruption. This chapter argues that successfully navigating these innovations requires balancing three core tensions: (1) Implementation Feasibility, or the practical capacity to integrate AI sustainably into real classrooms; (2) Adaptation Speed, or the mismatch between fast-evolving AI capabilities and the slower pace of educational change; and (3) Mission Alignment, or the need to ensure AI applications uphold educational values such as equity, privacy, and pedagogical integrity. First, we review early evidence of generative and agentic AI in various sectors and in frontline education to…
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