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arxiv: 2604.19788 · v1 · submitted 2026-04-01 · 💻 cs.AI · cs.HC

Using Learning Theories to Evolve Human-Centered XAI: Future Perspectives and Challenges

Karina Cortinas-Lorenzo , Gavin Doherty This is my paper

Pith reviewed 2026-05-13 23:03 UTC · model grok-4.3

classification 💻 cs.AI cs.HC
keywords explainable AIlearning theorieshuman-centered XAIlearner-centered approachAI explanationshuman agencyXAI risksAI transparency
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The pith

Learning theories from human education can be integrated into XAI to create learner-centered explanations that increase human agency and simplify risk mitigation.

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

This position paper explores infusing established learning theories into the full lifecycle of Explainable AI systems, from assessment through design and evaluation. It focuses on shifting explanations from mere transparency tools toward supports for actual human learning in interactions with complex AI. A sympathetic reader would care because current XAI approaches often fail to help users build lasting understanding, leaving people passive or over-reliant on opaque models; a learner-centered shift could change that dynamic.

Core claim

The central claim is that a learner-centered approach to Explainable AI, built by applying learning theories to the XAI lifecycle, can enhance human agency and ease the mitigation of associated risks, thereby evolving the practice of human-centered XAI.

What carries the argument

The learner-centered approach to XAI, which treats explanations as scaffolds for human learning processes rather than static information transfers.

If this is right

  • Explanations would be assessed by how well they promote measurable learning outcomes rather than only immediate comprehension.
  • XAI design processes would incorporate educational principles such as scaffolding and active knowledge construction.
  • Risk mitigation would become easier because users with deeper understanding could better anticipate and handle AI failures.
  • Human agency would rise as people move from passive recipients of AI outputs to active learners who can question and adapt those outputs.

Where Pith is reading between the lines

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

  • XAI evaluation metrics might shift toward long-term retention and transfer of knowledge instead of snapshot accuracy scores.
  • Current XAI methods could be shown to treat users as information consumers rather than learners, limiting their effectiveness.
  • Adaptive XAI interfaces could emerge that adjust explanation style based on detected user learning progress, similar to tutoring systems.

Load-bearing premise

Learning theories created for human-to-human teaching apply directly and usefully to how people learn from AI explanations without major new adaptation or testing.

What would settle it

An empirical study comparing learner-centered XAI explanations against standard ones and finding no measurable gains in user agency, learning retention, or risk reduction.

read the original abstract

As Artificial Intelligence (AI) systems continue to grow in size and complexity, so does the difficulty of the quest for AI transparency. In a world of large models and complex AI systems, why do we explain AI and what should we explain? While explanations serve multiple functions, in the face of complexity humans have used and continue to use explanations to foster learning. In this position paper, we discuss how learning theories can be infused in the XAI lifecycle, as well as the key opportunities and challenges when adopting a learner-centered approach to assess, design and evaluate AI explanations. Building on past work, we argue that a learner-centered approach to Explainable AI (XAI) can enhance human agency and ease XAI risks mitigation, helping evolve the practice of human-centered XAI.

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 / 3 minor

Summary. This position paper argues for infusing learning theories from education into the XAI lifecycle to support a learner-centered approach for assessing, designing, and evaluating AI explanations. Building on prior work, it claims that this perspective can enhance human agency, mitigate risks in complex AI systems, and evolve human-centered XAI practices, while outlining key opportunities and challenges.

Significance. If the proposed synthesis holds, the paper offers a useful conceptual bridge between educational psychology and XAI, potentially guiding more effective explanation design that prioritizes human learning outcomes over mere transparency. Its value lies in the interdisciplinary framing and explicit discussion of future challenges rather than any empirical demonstration or formal derivation.

major comments (1)
  1. [Abstract and opportunities discussion] The central claim in the abstract and introduction rests on the transfer of human-to-human learning theories to human-AI explanation settings; while the manuscript frames this as a discussion of opportunities rather than a validated mapping, the absence of any concrete adaptation mechanisms or counter-examples in the opportunities section leaves the load-bearing assumption unexamined and risks overgeneralization.
minor comments (3)
  1. The manuscript would benefit from at least one worked example mapping a specific learning theory (e.g., constructivism or situated learning) to an existing XAI technique such as counterfactual explanations.
  2. [Related work] Several citations to foundational XAI surveys appear dated; updating the related-work section with post-2022 human-AI interaction studies would strengthen the positioning.
  3. [Challenges] The challenges section lists high-level issues but does not discuss measurement approaches for assessing whether explanations actually foster learning; adding a short paragraph on potential evaluation metrics would improve clarity.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for their thoughtful review and constructive suggestion. We agree that strengthening the opportunities discussion with more concrete elements will improve the manuscript and will make targeted revisions to address this point.

read point-by-point responses

  1. Referee: [Abstract and opportunities discussion] The central claim in the abstract and introduction rests on the transfer of human-to-human learning theories to human-AI explanation settings; while the manuscript frames this as a discussion of opportunities rather than a validated mapping, the absence of any concrete adaptation mechanisms or counter-examples in the opportunities section leaves the load-bearing assumption unexamined and risks overgeneralization.

    Authors: We appreciate this observation. As a position paper, the manuscript deliberately frames the discussion as exploratory opportunities rather than a validated transfer. However, we acknowledge that the opportunities section would benefit from greater specificity to reduce the risk of overgeneralization. In the revised version, we will add brief concrete adaptation mechanisms (e.g., how constructivist scaffolding principles could be operationalized in interactive XAI interfaces) and at least one counter-example where direct transfer from human-to-human learning may not apply (e.g., differences in cognitive load when explanations are generated by opaque models). These additions will be kept concise and clearly labeled as illustrative rather than exhaustive. revision: yes

Circularity Check

0 steps flagged

No significant circularity; position paper relies on conceptual synthesis

full rationale

This is a position paper whose central argument is a proposal to infuse learning theories into the XAI lifecycle to enhance human agency. It contains no equations, fitted parameters, derivations, or technical reductions. All claims are framed as discussion of opportunities and challenges drawn from prior literature rather than any self-referential chain that reduces a result to its own inputs by construction. Self-citations serve only as background and are not load-bearing for any derivation.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on the domain assumption that learning theories developed outside AI can be productively mapped onto explanation design without introducing new untested constructs.

axioms (1)
  • domain assumption Learning theories can be infused into the XAI lifecycle to foster human learning from AI explanations.
    Invoked throughout the abstract as the basis for the learner-centered approach.

pith-pipeline@v0.9.0 · 5427 in / 1181 out tokens · 67555 ms · 2026-05-13T23:03:45.083341+00:00 · methodology

discussion (0)

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Reference graph

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