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arxiv: 2604.27245 · v2 · pith:BL2MVV46new · submitted 2026-04-29 · 💻 cs.CY · cs.AI

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) This is my paper

Pith reviewed 2026-05-20 23:48 UTC · model grok-4.3

classification 💻 cs.CY cs.AI
keywords agentic AIAI in educationimplementation frameworkfeasibility tensionadaptation speedmission alignmentK-12 educationhigher education
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The pith

Successfully adopting agentic AI in education requires balancing implementation feasibility, adaptation speed, and mission alignment.

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

Generative AI reached classrooms ahead of institutional readiness, and the next wave of agentic systems that can plan and pursue goals independently brings both stronger personalization and bigger risks of disruption. The paper claims that handling this shift means addressing three linked tensions rather than treating AI adoption as a simple technical upgrade. Implementation feasibility asks whether schools can add the technology without exhausting resources or breaking daily routines. Adaptation speed points to the mismatch between rapid AI progress and the slower cycles of curriculum change and teacher training. Mission alignment insists that any deployment must protect equity, privacy, and established teaching values. The authors review early uses across sectors and education to make the tensions concrete, then supply the framework with application examples for K-12 and higher education.

Core claim

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.

What carries the argument

The three-tension framework that guides decision-makers in evaluating and designing responsible AI deployments across K-12 and higher education by weighing feasibility, pace of change, and value alignment.

If this is right

  • The framework supplies concrete steps for planning AI initiatives that fit existing classroom structures.
  • It directs attention to the gap between AI development cycles and education system timelines.
  • It requires explicit checks that proposed AI tools support equity, privacy, and pedagogical goals.
  • Trends such as curriculum-linked AI agents and educator-informed design emerge as practical applications of the framework.
  • Educational leaders receive recommendations for proactive engagement that can shape the next decade of teaching and learning.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The framework could be piloted in a small number of districts to track whether explicit tension management changes adoption outcomes or teacher satisfaction.
  • Similar tensions may appear in other regulated domains such as healthcare or social services when agentic AI is introduced.
  • Developing simple scoring rubrics for each tension would allow leaders to compare options more systematically.
  • Ongoing collaboration between AI builders and practicing educators could reduce the adaptation-speed tension before deployments occur.

Load-bearing premise

That the three identified tensions are the primary and sufficient set of considerations for guiding decision-makers in evaluating and designing responsible AI deployments across K-12 and higher education.

What would settle it

A documented case in which multiple schools or universities adopt agentic AI systems at scale while ignoring one or more of the three tensions and still achieve sustained, value-preserving results.

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

1 major / 2 minor

Summary. The manuscript proposes a prescriptive three-tension framework to guide responsible adoption of agentic AI systems in K-12 and higher education. The tensions are Implementation Feasibility (practical integration capacity), Adaptation Speed (mismatch between AI evolution and institutional change), and Mission Alignment (upholding equity, privacy, and pedagogical values). It reviews early evidence from sectors and education to illustrate the tensions, presents the framework with application examples, identifies trends such as curriculum-linked AI agents, and concludes with recommendations for educational leaders.

Significance. If the framework's central claim holds, it could provide a structured lens for decision-makers to evaluate AI initiatives and reduce the gap between rapid technological change and slower educational adaptation. The manuscript's strengths include its synthesis of early cross-sector evidence and concrete examples of framework application to planning responsible deployments; these elements make the work potentially useful for practitioners even in the absence of new empirical data.

major comments (1)
  1. Abstract and framework presentation (following the evidence review): The claim that 'successfully navigating these innovations requires balancing three core tensions' is load-bearing for the paper's contribution, yet the selection process is not shown to be exhaustive or justified. No mapping of candidate tensions (e.g., regulatory lag, data sovereignty, or labor displacement), no stakeholder elicitation, and no argument that omitted factors are subsumed under the three are provided; the framework therefore rests on illustrative examples rather than a demonstrated completeness argument.
minor comments (2)
  1. The abstract introduces 'agentic AI' and 'generative AI' without a concise operational definition or distinction, which may reduce accessibility for readers from education policy backgrounds.
  2. Application examples in the framework section would benefit from explicit criteria or rubrics for how decision-makers should weigh or resolve conflicts among the three tensions in practice.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for their constructive feedback on our manuscript. The comment raises a valid point about the justification for the three-tension framework, and we address it directly below while proposing a targeted revision to strengthen the presentation.

read point-by-point responses

  1. Referee: Abstract and framework presentation (following the evidence review): The claim that 'successfully navigating these innovations requires balancing three core tensions' is load-bearing for the paper's contribution, yet the selection process is not shown to be exhaustive or justified. No mapping of candidate tensions (e.g., regulatory lag, data sovereignty, or labor displacement), no stakeholder elicitation, and no argument that omitted factors are subsumed under the three are provided; the framework therefore rests on illustrative examples rather than a demonstrated completeness argument.

    Authors: We agree that the manuscript would benefit from greater transparency on how the three tensions were selected. The framework is presented as a conceptual synthesis derived from the preceding evidence review of generative and agentic AI applications across sectors and in education, where the recurring themes of practical integration barriers, temporal mismatches in adaptation, and value conflicts emerged as central. We do not claim the framework is exhaustive or derived from formal stakeholder elicitation or a comprehensive candidate mapping, as this is a synthesis chapter rather than an empirical study. In the revised version, we will insert a short subsection immediately before the framework presentation that (a) briefly describes the inductive process from the evidence review, (b) provides a concise mapping showing how omitted factors such as regulatory lag, data sovereignty, and labor displacement intersect with or are subsumed under the three tensions (e.g., regulatory lag primarily affects Adaptation Speed and Mission Alignment), and (c) explicitly states the framework's scope as a practical decision-making lens rather than a complete taxonomy. This revision will make the load-bearing claim more robust without altering the core contribution. revision: yes

Circularity Check

0 steps flagged

Three-tension framework is a conceptual synthesis without circular reduction

full rationale

The paper reviews early evidence from sectors and education to illustrate the three tensions, then presents the framework as a guide for decision-makers with application examples. No equations, fitted parameters, predictions, or self-referential definitions exist that reduce any claim to its own inputs by construction. The central assertion that navigating agentic AI requires balancing these tensions is an argumentative proposal, not a derivation that loops back via self-citation chains or ansatzes. The work draws on general external evidence and remains self-contained without load-bearing reductions to prior unverified assertions by the same authors.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The paper rests on domain assumptions about the nature of educational change and the primacy of the three tensions; no free parameters or invented entities are introduced.

axioms (1)
  • domain assumption The three tensions (Implementation Feasibility, Adaptation Speed, Mission Alignment) are the core considerations that must be balanced for successful agentic AI adoption in education.
    This premise is invoked directly in the argument that navigating innovations requires balancing these specific tensions.

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Works this paper leans on

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