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arxiv: 2604.27245 · v1 · submitted 2026-04-29 · 💻 cs.CY · cs.AI

Recognition: unknown

Addressing the Reality Gap: A Three-Tension Framework for Agentic AI Adoption

Jason Fournier (Imagine Learning), Kacper {\L}odzikowski (Adam Mickiewicz University, Poland), Pozna\'n

Pith reviewed 2026-05-07 09:29 UTC · model grok-4.3

classification 💻 cs.CY cs.AI
keywords agentic AIAI adoptioneducational technologyimplementation feasibilityadaptation speedmission alignmentreality gapresponsible AI
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0 comments X

The pith

Three tensions must be balanced to successfully adopt agentic AI in education.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper establishes that the arrival of autonomous agentic AI systems creates a reality gap because their speed outpaces schools' ability to respond. It proposes that decision-makers must balance three tensions: implementation feasibility for sustainable classroom integration, adaptation speed to handle the mismatch between rapid AI change and slower institutional pace, and mission alignment to protect values like equity, privacy, and pedagogical integrity. The authors review early evidence from sectors and education, then show how the framework guides evaluation and design of AI initiatives in K-12 and higher education. They illustrate its use with planning examples and note trends such as curriculum-linked agents and educator-informed design. A sympathetic reader cares because the framework offers a practical structure for turning AI personalization potential into actual teaching improvements without unintended disruption.

Core claim

The paper's central claim is that successfully navigating agentic AI innovations in education requires balancing three core tensions: Implementation Feasibility, the practical capacity to integrate AI sustainably into real classrooms; Adaptation Speed, the mismatch between fast-evolving AI capabilities and the slower pace of educational change; and Mission Alignment, the need to ensure AI applications uphold educational values such as equity, privacy, and pedagogical integrity. After reviewing early evidence from various sectors and frontline education, the framework is presented to guide decision-makers in evaluating and designing AI initiatives across K-12 and higher education, with worked

What carries the argument

The three-tension framework, which acts as a structured evaluation tool for weighing practical integration capacity, temporal mismatch, and value preservation when planning AI deployments.

Load-bearing premise

That the three identified tensions comprehensively capture the key challenges in agentic AI adoption for education and that applying the framework will lead to better outcomes without needing additional dimensions or empirical testing.

What would settle it

A comparison study tracking AI adoption projects in multiple schools, measuring whether those explicitly using the three tensions achieve measurably higher sustainability, equity metrics, and learning gains than projects ignoring the framework.

read the original 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 illustrate these tensions in context. Then, we present a three-tension framework to guide decision-makers in evaluating and designing AI initiatives across K-12 and higher education. We provide examples of how the framework can be applied to plan responsible AI deployments, and we identify emerging trends--such as curriculum-linked AI agents and educator-informed AI design--along with open research directions. We conclude the chapter with recommendations for educational leaders to proactively engage with the opportunities and challenges of AI, so that this technology can be harnessed to enhance teaching and learning in the decade ahead.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The paper reviews early evidence of generative and agentic AI applications across sectors and in education to illustrate challenges, then proposes a three-tension framework—Implementation Feasibility, Adaptation Speed, and Mission Alignment—for guiding responsible adoption of agentic AI in K-12 and higher education. It provides application examples for planning deployments, identifies trends such as curriculum-linked agents and educator-informed design, and offers recommendations for educational leaders.

Significance. If the framework is shown to be robust, it could supply educational decision-makers with a concise, actionable structure for weighing practical integration limits against rapid capability growth and core values like equity and privacy. The synthesis of early evidence provides a useful snapshot of an emerging domain, though the absence of validation leaves its prescriptive value untested.

major comments (2)
  1. [three-tension framework presentation] In the section presenting the three-tension framework (immediately following the evidence review), the assertion that Implementation Feasibility, Adaptation Speed, and Mission Alignment constitute the core and sufficient tensions is supported solely by selected illustrative cases; no systematic method (e.g., literature-mapping protocol, failure-case analysis, or stakeholder elicitation) is supplied to demonstrate exhaustiveness or dominance over alternatives such as regulatory lag or data sovereignty.
  2. [framework application examples] In the examples of framework application (the section following framework presentation), the scenarios describe intended uses but contain no measured outcomes, success metrics, or counterfactual comparisons, leaving unsupported the claim that applying the framework produces better decisions or more responsible deployments.
minor comments (2)
  1. [abstract] The abstract and introduction could more clearly distinguish the paper's conceptual contribution from empirical validation to manage reader expectations.
  2. [framework presentation] Terminology for the three tensions is introduced narratively; adding a concise table or boxed definition early in the framework section would improve referenceability across the text.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive comments on our manuscript. We address each major comment below, clarifying the conceptual nature of the work and making targeted revisions where appropriate.

read point-by-point responses

  1. Referee: In the section presenting the three-tension framework (immediately following the evidence review), the assertion that Implementation Feasibility, Adaptation Speed, and Mission Alignment constitute the core and sufficient tensions is supported solely by selected illustrative cases; no systematic method (e.g., literature-mapping protocol, failure-case analysis, or stakeholder elicitation) is supplied to demonstrate exhaustiveness or dominance over alternatives such as regulatory lag or data sovereignty.

    Authors: We acknowledge that the framework is not derived from a formal systematic review, literature-mapping protocol, or stakeholder elicitation process. It emerges from a synthesis of recurring challenges observed in the reviewed early evidence from multiple sectors and educational contexts. The three tensions were selected because they directly address the practical capacity to deploy, the temporal mismatch in capabilities versus institutional change, and the alignment with educational values—issues that appeared dominant in the cases examined. We do not claim these are exhaustive or that they dominate all alternatives; regulatory lag and data sovereignty are noted as related considerations in the open research directions section. In the revised manuscript, we have added a short subsection explaining the evidence-based rationale for the framework and explicitly stating its scope as a practical heuristic rather than a comprehensive or validated model. revision: partial

  2. Referee: In the examples of framework application (the section following framework presentation), the scenarios describe intended uses but contain no measured outcomes, success metrics, or counterfactual comparisons, leaving unsupported the claim that applying the framework produces better decisions or more responsible deployments.

    Authors: The examples are constructed as hypothetical planning scenarios to demonstrate how the framework could be applied in decision-making contexts; they are not drawn from completed deployments with empirical data. The manuscript does not make an empirical claim that use of the framework has been shown to produce better decisions or more responsible outcomes. It positions the framework as a tool to support structured evaluation of trade-offs. We have revised the examples section to state this illustrative intent more explicitly, removed any implication of proven superiority, and added a forward-looking note on the value of future empirical studies to test the framework's utility in real deployments. revision: yes

Circularity Check

0 steps flagged

No circularity: framework is proposed from external literature review without self-referential reduction or fitted predictions.

full rationale

The paper presents a three-tension framework as an argumentative synthesis drawn from reviewed evidence on AI in education and other sectors. No mathematical derivations, parameter fitting, equations, or self-citations appear in the provided text. The central claim—that balancing Implementation Feasibility, Adaptation Speed, and Mission Alignment guides adoption—is advanced by illustration and recommendation rather than by reducing to its own inputs or prior author work by construction. This is a standard conceptual proposal whose validity rests on the quality of the external review, not on internal definitional closure.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

The framework rests on assumptions about the nature of AI development and educational systems rather than new empirical data or derivations.

axioms (2)
  • domain assumption Agentic AI systems have the capacity to plan and act towards goals and will continue to evolve rapidly.
    Assumed in the introduction to the new wave of AI.
  • domain assumption Educational institutions change at a slower pace than AI capabilities.
    Core to the adaptation speed tension.

pith-pipeline@v0.9.0 · 5558 in / 1273 out tokens · 48025 ms · 2026-05-07T09:29:05.411463+00:00 · methodology

discussion (0)

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Reference graph

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