{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:MXWFHCPBPWGWVRTWDCN6IZWS2I","short_pith_number":"pith:MXWFHCPB","schema_version":"1.0","canonical_sha256":"65ec5389e17d8d6ac676189be466d2d23640895b57f34e0d6b09c044712e6b6d","source":{"kind":"arxiv","id":"2502.02945","version":1},"attestation_state":"computed","paper":{"title":"LLM-KT: Aligning Large Language Models with Knowledge Tracing using a Plug-and-Play Instruction","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Aimin Zhou, Bo Jiang, Jie Zhou, Liang He, Min Zhang, Qin Chen, Qinchun Bai, Ziwei Wang","submitted_at":"2025-02-05T07:21:49Z","abstract_excerpt":"The knowledge tracing (KT) problem is an extremely important topic in personalized education, which aims to predict whether students can correctly answer the next question based on their past question-answer records. Prior work on this task mainly focused on learning the sequence of behaviors based on the IDs or textual information. However, these studies usually fail to capture students' sufficient behavioral patterns without reasoning with rich world knowledge about questions. In this paper, we propose a large language models (LLMs)-based framework for KT, named \\texttt{\\textbf{LLM-KT}}, to "},"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":"2502.02945","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2025-02-05T07:21:49Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"eb3225855cba1538f866942c65ba1f3676891f33dcf3a96fd4ba6013fd27967a","abstract_canon_sha256":"f45861748238d1e24a1dc3239c45a430f0d9b0ae86903c929404d41869c94d46"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:09:57.277160Z","signature_b64":"lA5Vad+hTn1ZrNgMOKFeg70GyD8pRrpucc7Je9hPIf8trv7NFBrr9IkRSp5f63gO8BPEpII4NuRhp4ToEuK1Dg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"65ec5389e17d8d6ac676189be466d2d23640895b57f34e0d6b09c044712e6b6d","last_reissued_at":"2026-07-05T10:09:57.276760Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:09:57.276760Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"LLM-KT: Aligning Large Language Models with Knowledge Tracing using a Plug-and-Play Instruction","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Aimin Zhou, Bo Jiang, Jie Zhou, Liang He, Min Zhang, Qin Chen, Qinchun Bai, Ziwei Wang","submitted_at":"2025-02-05T07:21:49Z","abstract_excerpt":"The knowledge tracing (KT) problem is an extremely important topic in personalized education, which aims to predict whether students can correctly answer the next question based on their past question-answer records. Prior work on this task mainly focused on learning the sequence of behaviors based on the IDs or textual information. However, these studies usually fail to capture students' sufficient behavioral patterns without reasoning with rich world knowledge about questions. In this paper, we propose a large language models (LLMs)-based framework for KT, named \\texttt{\\textbf{LLM-KT}}, to "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2502.02945","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/2502.02945/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":"2502.02945","created_at":"2026-07-05T10:09:57.276812+00:00"},{"alias_kind":"arxiv_version","alias_value":"2502.02945v1","created_at":"2026-07-05T10:09:57.276812+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2502.02945","created_at":"2026-07-05T10:09:57.276812+00:00"},{"alias_kind":"pith_short_12","alias_value":"MXWFHCPBPWGW","created_at":"2026-07-05T10:09:57.276812+00:00"},{"alias_kind":"pith_short_16","alias_value":"MXWFHCPBPWGWVRTW","created_at":"2026-07-05T10:09:57.276812+00:00"},{"alias_kind":"pith_short_8","alias_value":"MXWFHCPB","created_at":"2026-07-05T10:09:57.276812+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.29049","citing_title":"MOSAIC: Orchestrating Collaborative Knowledge Tracing with Hierarchical Semantic Alignment","ref_index":67,"is_internal_anchor":false},{"citing_arxiv_id":"2509.21514","citing_title":"Knowing When to Defer: Selective Prediction for Responsible Knowledge Tracing","ref_index":7,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/MXWFHCPBPWGWVRTWDCN6IZWS2I","json":"https://pith.science/pith/MXWFHCPBPWGWVRTWDCN6IZWS2I.json","graph_json":"https://pith.science/api/pith-number/MXWFHCPBPWGWVRTWDCN6IZWS2I/graph.json","events_json":"https://pith.science/api/pith-number/MXWFHCPBPWGWVRTWDCN6IZWS2I/events.json","paper":"https://pith.science/paper/MXWFHCPB"},"agent_actions":{"view_html":"https://pith.science/pith/MXWFHCPBPWGWVRTWDCN6IZWS2I","download_json":"https://pith.science/pith/MXWFHCPBPWGWVRTWDCN6IZWS2I.json","view_paper":"https://pith.science/paper/MXWFHCPB","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2502.02945&json=true","fetch_graph":"https://pith.science/api/pith-number/MXWFHCPBPWGWVRTWDCN6IZWS2I/graph.json","fetch_events":"https://pith.science/api/pith-number/MXWFHCPBPWGWVRTWDCN6IZWS2I/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/MXWFHCPBPWGWVRTWDCN6IZWS2I/action/timestamp_anchor","attest_storage":"https://pith.science/pith/MXWFHCPBPWGWVRTWDCN6IZWS2I/action/storage_attestation","attest_author":"https://pith.science/pith/MXWFHCPBPWGWVRTWDCN6IZWS2I/action/author_attestation","sign_citation":"https://pith.science/pith/MXWFHCPBPWGWVRTWDCN6IZWS2I/action/citation_signature","submit_replication":"https://pith.science/pith/MXWFHCPBPWGWVRTWDCN6IZWS2I/action/replication_record"}},"created_at":"2026-07-05T10:09:57.276812+00:00","updated_at":"2026-07-05T10:09:57.276812+00:00"}