{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:VQFXNL3R4AZ5I64BOSQ4JVBNYL","short_pith_number":"pith:VQFXNL3R","schema_version":"1.0","canonical_sha256":"ac0b76af71e033d47b8174a1c4d42dc2f314355586d0c85ae1d28cdc875d40b7","source":{"kind":"arxiv","id":"2302.07427","version":2},"attestation_state":"computed","paper":{"title":"Studying the effect of AI Code Generators on Supporting Novice Learners in Introductory Programming","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.HC","authors_text":"Barbara J. Ericson, Carl Ka To Ma, David Weintrop, Justin Chow, Majeed Kazemitabaar, Tovi Grossman","submitted_at":"2023-02-15T02:17:39Z","abstract_excerpt":"AI code generators like OpenAI Codex have the potential to assist novice programmers by generating code from natural language descriptions, however, over-reliance might negatively impact learning and retention. To explore the implications that AI code generators have on introductory programming, we conducted a controlled experiment with 69 novices (ages 10-17). Learners worked on 45 Python code-authoring tasks, for which half of the learners had access to Codex, each followed by a code-modification task. Our results show that using Codex significantly increased code-authoring performance (1.15"},"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":"2302.07427","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.HC","submitted_at":"2023-02-15T02:17:39Z","cross_cats_sorted":[],"title_canon_sha256":"baae444efd3f1ebffd46f4b888723f1f5e62485e1a81c9fbefa3a2af3297b731","abstract_canon_sha256":"130977156067256de117a87bf03e315793bff6cee71a86c62d686090f60cc521"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:44:16.077343Z","signature_b64":"/Z2B2FkrF0RkSA2Bvr5igCYJOxQ8S99atlxgVCm5CW39SEnSJ0pPykuJJcsbaG9h36zv46Epf+buOG5+ffr1BQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ac0b76af71e033d47b8174a1c4d42dc2f314355586d0c85ae1d28cdc875d40b7","last_reissued_at":"2026-07-05T05:44:16.076923Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:44:16.076923Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Studying the effect of AI Code Generators on Supporting Novice Learners in Introductory Programming","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.HC","authors_text":"Barbara J. Ericson, Carl Ka To Ma, David Weintrop, Justin Chow, Majeed Kazemitabaar, Tovi Grossman","submitted_at":"2023-02-15T02:17:39Z","abstract_excerpt":"AI code generators like OpenAI Codex have the potential to assist novice programmers by generating code from natural language descriptions, however, over-reliance might negatively impact learning and retention. To explore the implications that AI code generators have on introductory programming, we conducted a controlled experiment with 69 novices (ages 10-17). Learners worked on 45 Python code-authoring tasks, for which half of the learners had access to Codex, each followed by a code-modification task. Our results show that using Codex significantly increased code-authoring performance (1.15"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2302.07427","kind":"arxiv","version":2},"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/2302.07427/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":"2302.07427","created_at":"2026-07-05T05:44:16.076981+00:00"},{"alias_kind":"arxiv_version","alias_value":"2302.07427v2","created_at":"2026-07-05T05:44:16.076981+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2302.07427","created_at":"2026-07-05T05:44:16.076981+00:00"},{"alias_kind":"pith_short_12","alias_value":"VQFXNL3R4AZ5","created_at":"2026-07-05T05:44:16.076981+00:00"},{"alias_kind":"pith_short_16","alias_value":"VQFXNL3R4AZ5I64B","created_at":"2026-07-05T05:44:16.076981+00:00"},{"alias_kind":"pith_short_8","alias_value":"VQFXNL3R","created_at":"2026-07-05T05:44:16.076981+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2411.09916","citing_title":"\"Should I Give Up Now?\" Investigating LLM Pitfalls in Software Engineering","ref_index":4,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/VQFXNL3R4AZ5I64BOSQ4JVBNYL","json":"https://pith.science/pith/VQFXNL3R4AZ5I64BOSQ4JVBNYL.json","graph_json":"https://pith.science/api/pith-number/VQFXNL3R4AZ5I64BOSQ4JVBNYL/graph.json","events_json":"https://pith.science/api/pith-number/VQFXNL3R4AZ5I64BOSQ4JVBNYL/events.json","paper":"https://pith.science/paper/VQFXNL3R"},"agent_actions":{"view_html":"https://pith.science/pith/VQFXNL3R4AZ5I64BOSQ4JVBNYL","download_json":"https://pith.science/pith/VQFXNL3R4AZ5I64BOSQ4JVBNYL.json","view_paper":"https://pith.science/paper/VQFXNL3R","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2302.07427&json=true","fetch_graph":"https://pith.science/api/pith-number/VQFXNL3R4AZ5I64BOSQ4JVBNYL/graph.json","fetch_events":"https://pith.science/api/pith-number/VQFXNL3R4AZ5I64BOSQ4JVBNYL/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/VQFXNL3R4AZ5I64BOSQ4JVBNYL/action/timestamp_anchor","attest_storage":"https://pith.science/pith/VQFXNL3R4AZ5I64BOSQ4JVBNYL/action/storage_attestation","attest_author":"https://pith.science/pith/VQFXNL3R4AZ5I64BOSQ4JVBNYL/action/author_attestation","sign_citation":"https://pith.science/pith/VQFXNL3R4AZ5I64BOSQ4JVBNYL/action/citation_signature","submit_replication":"https://pith.science/pith/VQFXNL3R4AZ5I64BOSQ4JVBNYL/action/replication_record"}},"created_at":"2026-07-05T05:44:16.076981+00:00","updated_at":"2026-07-05T05:44:16.076981+00:00"}