{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:FS4LDRVH55M6MODJE73UAWGU5M","short_pith_number":"pith:FS4LDRVH","schema_version":"1.0","canonical_sha256":"2cb8b1c6a7ef59e6386927f74058d4eb3aafa1038f0a1610c0a9a99a79215196","source":{"kind":"arxiv","id":"2504.14222","version":1},"attestation_state":"computed","paper":{"title":"tAIfa: Enhancing Team Effectiveness and Cohesion with AI-Generated Automated Feedback","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.HC","authors_text":"Charles Chiang, Diego Gomez-Zara, Mohammed Almutairi, Yuxin Bai","submitted_at":"2025-04-19T08:06:48Z","abstract_excerpt":"Providing timely and actionable feedback is crucial for effective collaboration, learning, and coordination within teams. However, many teams face challenges in receiving feedback that aligns with their goals and promotes cohesion. We introduce tAIfa (``Team AI Feedback Assistant''), an AI agent that uses Large Language Models (LLMs) to provide personalized, automated feedback to teams and their members. tAIfa analyzes team interactions, identifies strengths and areas for improvement, and delivers targeted feedback based on communication patterns. We conducted a between-subjects study with 18 "},"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":"2504.14222","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.HC","submitted_at":"2025-04-19T08:06:48Z","cross_cats_sorted":[],"title_canon_sha256":"9b79ee81625694cd69dde4738932f9d7f3b72ef063e121f13e3be41ca56250f0","abstract_canon_sha256":"0355bf3e0ff87e7528e1572e695001babee53c517762d5484ef7fad063717786"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:52:20.150246Z","signature_b64":"Csqs27AEa9w6Bf1D141uoWPhpIvh7Z8GZeuOda1GLJuQJ2YWuG7+Q2kP0fCtVgSlsqqNCBlvHAoSPx8TskdXAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2cb8b1c6a7ef59e6386927f74058d4eb3aafa1038f0a1610c0a9a99a79215196","last_reissued_at":"2026-07-05T11:52:20.149752Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:52:20.149752Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"tAIfa: Enhancing Team Effectiveness and Cohesion with AI-Generated Automated Feedback","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.HC","authors_text":"Charles Chiang, Diego Gomez-Zara, Mohammed Almutairi, Yuxin Bai","submitted_at":"2025-04-19T08:06:48Z","abstract_excerpt":"Providing timely and actionable feedback is crucial for effective collaboration, learning, and coordination within teams. However, many teams face challenges in receiving feedback that aligns with their goals and promotes cohesion. We introduce tAIfa (``Team AI Feedback Assistant''), an AI agent that uses Large Language Models (LLMs) to provide personalized, automated feedback to teams and their members. tAIfa analyzes team interactions, identifies strengths and areas for improvement, and delivers targeted feedback based on communication patterns. We conducted a between-subjects study with 18 "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2504.14222","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/2504.14222/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":"2504.14222","created_at":"2026-07-05T11:52:20.149820+00:00"},{"alias_kind":"arxiv_version","alias_value":"2504.14222v1","created_at":"2026-07-05T11:52:20.149820+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2504.14222","created_at":"2026-07-05T11:52:20.149820+00:00"},{"alias_kind":"pith_short_12","alias_value":"FS4LDRVH55M6","created_at":"2026-07-05T11:52:20.149820+00:00"},{"alias_kind":"pith_short_16","alias_value":"FS4LDRVH55M6MODJ","created_at":"2026-07-05T11:52:20.149820+00:00"},{"alias_kind":"pith_short_8","alias_value":"FS4LDRVH","created_at":"2026-07-05T11:52:20.149820+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2506.09420","citing_title":"A Call for Collaborative Intelligence: Why Human-Agent Systems Should Precede AI Autonomy","ref_index":1,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/FS4LDRVH55M6MODJE73UAWGU5M","json":"https://pith.science/pith/FS4LDRVH55M6MODJE73UAWGU5M.json","graph_json":"https://pith.science/api/pith-number/FS4LDRVH55M6MODJE73UAWGU5M/graph.json","events_json":"https://pith.science/api/pith-number/FS4LDRVH55M6MODJE73UAWGU5M/events.json","paper":"https://pith.science/paper/FS4LDRVH"},"agent_actions":{"view_html":"https://pith.science/pith/FS4LDRVH55M6MODJE73UAWGU5M","download_json":"https://pith.science/pith/FS4LDRVH55M6MODJE73UAWGU5M.json","view_paper":"https://pith.science/paper/FS4LDRVH","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2504.14222&json=true","fetch_graph":"https://pith.science/api/pith-number/FS4LDRVH55M6MODJE73UAWGU5M/graph.json","fetch_events":"https://pith.science/api/pith-number/FS4LDRVH55M6MODJE73UAWGU5M/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/FS4LDRVH55M6MODJE73UAWGU5M/action/timestamp_anchor","attest_storage":"https://pith.science/pith/FS4LDRVH55M6MODJE73UAWGU5M/action/storage_attestation","attest_author":"https://pith.science/pith/FS4LDRVH55M6MODJE73UAWGU5M/action/author_attestation","sign_citation":"https://pith.science/pith/FS4LDRVH55M6MODJE73UAWGU5M/action/citation_signature","submit_replication":"https://pith.science/pith/FS4LDRVH55M6MODJE73UAWGU5M/action/replication_record"}},"created_at":"2026-07-05T11:52:20.149820+00:00","updated_at":"2026-07-05T11:52:20.149820+00:00"}