{"paper":{"title":"Addressing the Reality Gap: A Three-Tension Framework for Agentic AI Adoption","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"Three tensions must be balanced to successfully adopt agentic AI in education.","cross_cats":["cs.AI"],"primary_cat":"cs.CY","authors_text":"Jason Fournier (Imagine Learning), Kacper {\\L}odzikowski (Adam Mickiewicz University, Poland), Pozna\\'n","submitted_at":"2026-04-29T22:33:36Z","abstract_excerpt":"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 capabiliti"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"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.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"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.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"A three-tension framework is introduced to help navigate the adoption of autonomous agentic AI systems in K-12 and higher education by addressing practical, temporal, and value-based challenges.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"Three tensions must be balanced to successfully adopt agentic AI in education.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"d3f470f8c43348d5c88f844d0cd9a12ffae5dd86086d4c5a607462feda2b4035"},"source":{"id":"2604.27245","kind":"arxiv","version":2},"verdict":{"id":"66d62aac-dd28-4145-a3e0-51c5c6375336","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-07T09:29:05.411463Z","strongest_claim":"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.","one_line_summary":"A three-tension framework is introduced to help navigate the adoption of autonomous agentic AI systems in K-12 and higher education by addressing practical, temporal, and value-based challenges.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"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.","pith_extraction_headline":"Three tensions must be balanced to successfully adopt agentic AI in education."},"integrity":{"clean":false,"summary":{"advisory":1,"critical":0,"by_detector":{"doi_compliance":{"total":1,"advisory":1,"critical":0,"informational":0}},"informational":0},"endpoint":"/pith/2604.27245/integrity.json","findings":[{"note":"DOI in the printed bibliography is fragmented by whitespace or line breaks. A longer candidate (10.1186/s40359-025-01865-4Weiner) was visible in the surrounding text but could not be confirmed against doi.org as printed.","detector":"doi_compliance","severity":"advisory","ref_index":55,"audited_at":"2026-05-19T19:26:23.279418Z","detected_doi":"10.1186/s40359-025-01865-4Weiner","finding_type":"recoverable_identifier","verdict_class":"incontrovertible","detected_arxiv_id":null}],"available":true,"detectors_run":[{"name":"doi_compliance","ran_at":"2026-05-19T19:26:23.279418Z","status":"completed","version":"1.0.0","findings_count":1}],"snapshot_sha256":"0e569b618aad903dc13ed17e30f5b177bb946bdc4666b66b3aad21a6305a8b04"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}