{"id":"3b79d30f-4b66-4d51-a1f6-282a0b015b20","arxiv_id":"2604.19788","paper_version":1,"verdict":"UNVERDICTED","confidence":"LOW","novelty_score":4.0,"correctness_risk":"low","formal_verification":"none","parameter_count":0,"one_line_summary":"Infusing learning theories into the XAI lifecycle offers a learner-centered path to improve human agency and mitigate explanation-related risks in AI systems.","lead":"The paper argues for integrating established learning theories into the design, assessment, and evaluation of explanations in Explainable AI systems. A learner-centered framing could strengthen human understanding and reduce risks in complex AI interactions.","discovery_kind":"new_application","skeptic_critique":{"model":"grok-4.3","headline":"No significant objection identified","rationale":"The reader's identified assumption correctly flags a direction for future empirical work, but the current manuscript does not assert that transfer has already occurred or is automatic. Its scope is to propose the reframing and surface challenges, so the argument is internally consistent within a position-paper genre. No load-bearing flaw in soundness or correctness is present.","tokens_in":1640,"tokens_out":235,"duration_ms":45258,"concrete_test":"Select one cited learning theory (e.g., constructivism) from the manuscript and derive a single concrete XAI interface modification that follows from it; check whether the paper's own challenges section already flags the adaptation steps needed.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The paper is a position paper whose central claim is an argument for infusing learning theories into the XAI lifecycle to enhance human agency. It explicitly frames the work as a discussion of opportunities and challenges rather than an empirical demonstration of transfer. No technical derivation, equation, or unstated assumption about direct applicability without adaptation is required for the stated claim to stand as a proposal.","agreement_with_reader":"disagree"},"referee_report":{"model":"grok-4.3","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.","tokens_in":1682,"tokens_out":408,"duration_ms":49032,"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":[{"comment":"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.","section":"Abstract and opportunities discussion"}],"minor_comments":[{"comment":"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.","section":null},{"comment":"Several citations to foundational XAI surveys appear dated; updating the related-work section with post-2022 human-AI interaction studies would strengthen the positioning.","section":"Related work"},{"comment":"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.","section":"Challenges"}],"recommendation":"minor_revision","confidential_remarks":null},"author_rebuttal":{"model":"grok-4.3","summary":"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.","responses":[{"response":"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_made":"yes","referee_comment":"[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."}],"tokens_in":1167,"tokens_out":282,"duration_ms":24613,"standing_objections":[]},"desk_editor":{"model":"grok-4.3","letter":"This is a position paper that pushes to bring learning theories into the XAI lifecycle so explanations better support human learning and agency. The core claim is that this learner-centered shift can improve human-centered XAI practice and help manage risks, framed as a discussion of opportunities and challenges rather than a tested method.","headline":"Position paper argues for linking learning theories to XAI design but stays conceptual with no new mappings or data.","tokens_in":2158,"tokens_out":127,"would_cite":false,"duration_ms":44259,"reading_group":"maybe","serious_thinker":"yes","would_accept_peer_review":true},"rs_alignment":{"model":"grok-4.3","evidence":[],"headline":"Position paper on learner-centered XAI has no overlap with RS forcing chain","alignment":"orthogonal","rationale":"The paper is a CHI position piece advocating infusion of classical learning theories (behavioral, constructivist, reflective, etc.) into XAI design/evaluation to support human agency. Its central machinery consists of pedagogical taxonomies and opportunity/challenge lists; it contains no recognition cost functions, ratio-symmetric derivations, golden-ratio identities, 8-tick periodicity, or parameter-free constant derivations. RS theorems such as reality_from_one_distinction (IndisputableMonolith/Foundation/RealityFromDistinction.lean) and the J-cost uniqueness results (IndisputableMonolith/Cost/FunctionalEquation.lean) therefore neither confirm nor contradict any claim in the paper.","tokens_in":44127,"confidence":"high","tokens_out":178,"duration_ms":8575,"cache_read_input_tokens":128,"cache_creation_input_tokens":0},"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"grok-4.3","headline":"Learning theories from human education can be integrated into XAI to create learner-centered explanations that increase human agency and simplify risk mitigation.","keywords":["explainable AI","learning theories","human-centered XAI","learner-centered approach","AI explanations","human agency","XAI risks","AI transparency"],"falsifier":"An empirical study comparing learner-centered XAI explanations against standard ones and finding no measurable gains in user agency, learning retention, or risk reduction.","tokens_in":2518,"feed_emoji":"📚","tokens_out":568,"duration_ms":25277,"temperature":0.7,"pith_summary":"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.","feed_headline":"Learning theories can guide better AI explanations","feed_subtitle":"A learner-centered approach to XAI aims to increase human agency and ease risk mitigation in complex systems.","key_machinery":"The learner-centered approach to XAI, which treats explanations as scaffolds for human learning processes rather than static information transfers.","core_discovery":"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.","pith_inferences":["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."],"forward_implications":["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."],"fun_headline_variants":["Learning theories evolve human-centered XAI","Learning theories in the XAI lifecycle","Learner-centered XAI with learning theories","Human agency via learning theories in XAI"],"cache_read_input_tokens":2112,"weakest_assumption_plain":"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.","fun_headline_variants_meta":{"raw":{"variants":["Learning theories evolve human-centered XAI","Learning theories in the XAI lifecycle","Learner-centered XAI with learning theories","Human agency via learning theories in XAI"]},"model":"grok-4.3","cost_usd":0.01017,"raw_usage":{"total_tokens":4368,"prompt_tokens":546,"num_sources_used":0,"completion_tokens":51,"cost_in_usd_ticks":101703000,"prompt_tokens_details":{"text_tokens":546,"audio_tokens":0,"image_tokens":0,"cached_tokens":64},"completion_tokens_details":{"audio_tokens":0,"reasoning_tokens":3771,"accepted_prediction_tokens":0,"rejected_prediction_tokens":0}},"tokens_in":546,"tokens_out":51,"duration_ms":109667,"temperature":1.0,"reasoning_tokens":3771,"cache_read_input_tokens":64,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-05-13T23:03:45.083341+00:00","model_set":{"reader":"grok-4.3"},"falsifier":"An empirical study comparing learner-centered XAI explanations against standard ones and finding no measurable gains in user agency, learning retention, or risk reduction.","supporting_citations":[],"review_version":1}