{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:JEWJS6LAGJG2EUPCYETA4NRM4Z","short_pith_number":"pith:JEWJS6LA","schema_version":"1.0","canonical_sha256":"492c997960324da251e2c1260e362ce64266bb915ae78f1b8e3fcb75ba2d1321","source":{"kind":"arxiv","id":"2506.20971","version":1},"attestation_state":"computed","paper":{"title":"Where is AIED Headed? Key Topics and Emerging Frontiers (2020-2024)","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.SI","authors_text":"Dragan Ga\\v{s}evi\\'c, Huilin Zhang, Shihui Feng","submitted_at":"2025-06-26T03:24:30Z","abstract_excerpt":"In this study, we analyze 2,398 research articles published between 2020 and 2024 across eight core venues related to the field of Artificial Intelligence in Education (AIED). Using a three-step knowledge co-occurrence network analysis, we analyze the knowledge structure of the field, the evolving knowledge clusters, and the emerging frontiers. Our findings reveal that AIED research remains strongly technically focused, with sustained themes such as intelligent tutoring systems, learning analytics, and natural language processing, alongside rising interest in large language models (LLMs) and g"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2506.20971","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.SI","submitted_at":"2025-06-26T03:24:30Z","cross_cats_sorted":[],"title_canon_sha256":"895956d402189ebd1b32263b73a29ec6bc82f51c7d2f398b4f0be7f828b0e1e4","abstract_canon_sha256":"4aaa8ae6eb19d7bffddd576b1e6424936383e9f7be7e043e31a1838908bffec6"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:27:36.947228Z","signature_b64":"jlgfahQ4gRBl/69oUhJ5td/y4eNzj3wzDl0sIxfJe0douXs1Oj3lKxRqDwQgUpet8zuhkq21yp5CbSDe6AtNCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"492c997960324da251e2c1260e362ce64266bb915ae78f1b8e3fcb75ba2d1321","last_reissued_at":"2026-07-05T11:27:36.946808Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:27:36.946808Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Where is AIED Headed? Key Topics and Emerging Frontiers (2020-2024)","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.SI","authors_text":"Dragan Ga\\v{s}evi\\'c, Huilin Zhang, Shihui Feng","submitted_at":"2025-06-26T03:24:30Z","abstract_excerpt":"In this study, we analyze 2,398 research articles published between 2020 and 2024 across eight core venues related to the field of Artificial Intelligence in Education (AIED). Using a three-step knowledge co-occurrence network analysis, we analyze the knowledge structure of the field, the evolving knowledge clusters, and the emerging frontiers. Our findings reveal that AIED research remains strongly technically focused, with sustained themes such as intelligent tutoring systems, learning analytics, and natural language processing, alongside rising interest in large language models (LLMs) and g"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.20971","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2506.20971/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2506.20971","created_at":"2026-07-05T11:27:36.946858+00:00"},{"alias_kind":"arxiv_version","alias_value":"2506.20971v1","created_at":"2026-07-05T11:27:36.946858+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.20971","created_at":"2026-07-05T11:27:36.946858+00:00"},{"alias_kind":"pith_short_12","alias_value":"JEWJS6LAGJG2","created_at":"2026-07-05T11:27:36.946858+00:00"},{"alias_kind":"pith_short_16","alias_value":"JEWJS6LAGJG2EUPC","created_at":"2026-07-05T11:27:36.946858+00:00"},{"alias_kind":"pith_short_8","alias_value":"JEWJS6LA","created_at":"2026-07-05T11:27:36.946858+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.05836","citing_title":"Can providing feedback on gaze and mental-effort synchrony improve pair programming performance?","ref_index":17,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/JEWJS6LAGJG2EUPCYETA4NRM4Z","json":"https://pith.science/pith/JEWJS6LAGJG2EUPCYETA4NRM4Z.json","graph_json":"https://pith.science/api/pith-number/JEWJS6LAGJG2EUPCYETA4NRM4Z/graph.json","events_json":"https://pith.science/api/pith-number/JEWJS6LAGJG2EUPCYETA4NRM4Z/events.json","paper":"https://pith.science/paper/JEWJS6LA"},"agent_actions":{"view_html":"https://pith.science/pith/JEWJS6LAGJG2EUPCYETA4NRM4Z","download_json":"https://pith.science/pith/JEWJS6LAGJG2EUPCYETA4NRM4Z.json","view_paper":"https://pith.science/paper/JEWJS6LA","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2506.20971&json=true","fetch_graph":"https://pith.science/api/pith-number/JEWJS6LAGJG2EUPCYETA4NRM4Z/graph.json","fetch_events":"https://pith.science/api/pith-number/JEWJS6LAGJG2EUPCYETA4NRM4Z/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/JEWJS6LAGJG2EUPCYETA4NRM4Z/action/timestamp_anchor","attest_storage":"https://pith.science/pith/JEWJS6LAGJG2EUPCYETA4NRM4Z/action/storage_attestation","attest_author":"https://pith.science/pith/JEWJS6LAGJG2EUPCYETA4NRM4Z/action/author_attestation","sign_citation":"https://pith.science/pith/JEWJS6LAGJG2EUPCYETA4NRM4Z/action/citation_signature","submit_replication":"https://pith.science/pith/JEWJS6LAGJG2EUPCYETA4NRM4Z/action/replication_record"}},"created_at":"2026-07-05T11:27:36.946858+00:00","updated_at":"2026-07-05T11:27:36.946858+00:00"}