{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:GA2NOKJ3KTN7Y4YWESBEC5RP6V","short_pith_number":"pith:GA2NOKJ3","canonical_record":{"source":{"id":"2505.01150","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CY","submitted_at":"2025-05-02T09:50:34Z","cross_cats_sorted":[],"title_canon_sha256":"f0cb499a5a2e3cdf0cee41ef68e442bbd281c4d4f0d61515fac3164dfebcea9b","abstract_canon_sha256":"d5a06a5b7c64becbfe606efe36c3bd17e9ab7c68fb19edd23f4f481149f6289c"},"schema_version":"1.0"},"canonical_sha256":"3034d7293b54dbfc7316248241762ff573022dfda27a392f0868b289a909b8ae","source":{"kind":"arxiv","id":"2505.01150","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2505.01150","created_at":"2026-07-05T10:57:46Z"},{"alias_kind":"arxiv_version","alias_value":"2505.01150v1","created_at":"2026-07-05T10:57:46Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.01150","created_at":"2026-07-05T10:57:46Z"},{"alias_kind":"pith_short_12","alias_value":"GA2NOKJ3KTN7","created_at":"2026-07-05T10:57:46Z"},{"alias_kind":"pith_short_16","alias_value":"GA2NOKJ3KTN7Y4YW","created_at":"2026-07-05T10:57:46Z"},{"alias_kind":"pith_short_8","alias_value":"GA2NOKJ3","created_at":"2026-07-05T10:57:46Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:GA2NOKJ3KTN7Y4YWESBEC5RP6V","target":"record","payload":{"canonical_record":{"source":{"id":"2505.01150","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CY","submitted_at":"2025-05-02T09:50:34Z","cross_cats_sorted":[],"title_canon_sha256":"f0cb499a5a2e3cdf0cee41ef68e442bbd281c4d4f0d61515fac3164dfebcea9b","abstract_canon_sha256":"d5a06a5b7c64becbfe606efe36c3bd17e9ab7c68fb19edd23f4f481149f6289c"},"schema_version":"1.0"},"canonical_sha256":"3034d7293b54dbfc7316248241762ff573022dfda27a392f0868b289a909b8ae","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:57:46.239848Z","signature_b64":"CvT+IxDbnj+KhYt8pFMHxcoJtGfnoL9XjyQPyCEEHmP3KnRd/A2Fzt6Yx+Ud6v5DxxwMeYzE47XjLmFcXk0dDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3034d7293b54dbfc7316248241762ff573022dfda27a392f0868b289a909b8ae","last_reissued_at":"2026-07-05T10:57:46.239332Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:57:46.239332Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2505.01150","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T10:57:46Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"zL8q011YIliYI3fTbGtAMrcPMTF2sF2mGXOYPwWkk/D9OGrdZahZekFsy9s5iS2bWiBY0f2sGRIXxE8j2l56DA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-08T07:33:48.480215Z"},"content_sha256":"cccfb90064ea48decc8b9b9784bc8a1ef92af57f727001b23da3178e237ddfb8","schema_version":"1.0","event_id":"sha256:cccfb90064ea48decc8b9b9784bc8a1ef92af57f727001b23da3178e237ddfb8"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:GA2NOKJ3KTN7Y4YWESBEC5RP6V","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Methodological Foundations for AI-Driven Survey Question Generation","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CY","authors_text":"Alexandra Werth, Campbell J. McColley, Kangxuan Rong, Ted K. Mburu","submitted_at":"2025-05-02T09:50:34Z","abstract_excerpt":"This paper presents a methodological framework for using generative AI in educational survey research. We explore how Large Language Models (LLMs) can generate adaptive, context-aware survey questions and introduce the Synthetic Question-Response Analysis (SQRA) framework, which enables iterative testing and refinement of AI-generated prompts prior to deployment with human participants. Guided by Activity Theory, we analyze how AI tools mediate participant engagement and learning, and we examine ethical issues such as bias, privacy, and transparency. Through sentiment, lexical, and structural "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.01150","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/2505.01150/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T10:57:46Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ricuyBtPcLtUGHm0vPQymo6pQyqIsQ4WJhT4tUvpv5v4g59wqnwoOtM+CRJdwVe7RL1BoVmk0bQp8KPxnHsYAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-08T07:33:48.480615Z"},"content_sha256":"2a5f8c40a700539d0edfe0b873cdef2e6110af0797d86dc588ded370df62d7b7","schema_version":"1.0","event_id":"sha256:2a5f8c40a700539d0edfe0b873cdef2e6110af0797d86dc588ded370df62d7b7"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/GA2NOKJ3KTN7Y4YWESBEC5RP6V/bundle.json","state_url":"https://pith.science/pith/GA2NOKJ3KTN7Y4YWESBEC5RP6V/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/GA2NOKJ3KTN7Y4YWESBEC5RP6V/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-07-08T07:33:48Z","links":{"resolver":"https://pith.science/pith/GA2NOKJ3KTN7Y4YWESBEC5RP6V","bundle":"https://pith.science/pith/GA2NOKJ3KTN7Y4YWESBEC5RP6V/bundle.json","state":"https://pith.science/pith/GA2NOKJ3KTN7Y4YWESBEC5RP6V/state.json","well_known_bundle":"https://pith.science/.well-known/pith/GA2NOKJ3KTN7Y4YWESBEC5RP6V/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:GA2NOKJ3KTN7Y4YWESBEC5RP6V","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"d5a06a5b7c64becbfe606efe36c3bd17e9ab7c68fb19edd23f4f481149f6289c","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CY","submitted_at":"2025-05-02T09:50:34Z","title_canon_sha256":"f0cb499a5a2e3cdf0cee41ef68e442bbd281c4d4f0d61515fac3164dfebcea9b"},"schema_version":"1.0","source":{"id":"2505.01150","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2505.01150","created_at":"2026-07-05T10:57:46Z"},{"alias_kind":"arxiv_version","alias_value":"2505.01150v1","created_at":"2026-07-05T10:57:46Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.01150","created_at":"2026-07-05T10:57:46Z"},{"alias_kind":"pith_short_12","alias_value":"GA2NOKJ3KTN7","created_at":"2026-07-05T10:57:46Z"},{"alias_kind":"pith_short_16","alias_value":"GA2NOKJ3KTN7Y4YW","created_at":"2026-07-05T10:57:46Z"},{"alias_kind":"pith_short_8","alias_value":"GA2NOKJ3","created_at":"2026-07-05T10:57:46Z"}],"graph_snapshots":[{"event_id":"sha256:2a5f8c40a700539d0edfe0b873cdef2e6110af0797d86dc588ded370df62d7b7","target":"graph","created_at":"2026-07-05T10:57:46Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2505.01150/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"This paper presents a methodological framework for using generative AI in educational survey research. We explore how Large Language Models (LLMs) can generate adaptive, context-aware survey questions and introduce the Synthetic Question-Response Analysis (SQRA) framework, which enables iterative testing and refinement of AI-generated prompts prior to deployment with human participants. Guided by Activity Theory, we analyze how AI tools mediate participant engagement and learning, and we examine ethical issues such as bias, privacy, and transparency. Through sentiment, lexical, and structural ","authors_text":"Alexandra Werth, Campbell J. McColley, Kangxuan Rong, Ted K. Mburu","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CY","submitted_at":"2025-05-02T09:50:34Z","title":"Methodological Foundations for AI-Driven Survey Question Generation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.01150","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:cccfb90064ea48decc8b9b9784bc8a1ef92af57f727001b23da3178e237ddfb8","target":"record","created_at":"2026-07-05T10:57:46Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"d5a06a5b7c64becbfe606efe36c3bd17e9ab7c68fb19edd23f4f481149f6289c","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CY","submitted_at":"2025-05-02T09:50:34Z","title_canon_sha256":"f0cb499a5a2e3cdf0cee41ef68e442bbd281c4d4f0d61515fac3164dfebcea9b"},"schema_version":"1.0","source":{"id":"2505.01150","kind":"arxiv","version":1}},"canonical_sha256":"3034d7293b54dbfc7316248241762ff573022dfda27a392f0868b289a909b8ae","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"3034d7293b54dbfc7316248241762ff573022dfda27a392f0868b289a909b8ae","first_computed_at":"2026-07-05T10:57:46.239332Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:57:46.239332Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"CvT+IxDbnj+KhYt8pFMHxcoJtGfnoL9XjyQPyCEEHmP3KnRd/A2Fzt6Yx+Ud6v5DxxwMeYzE47XjLmFcXk0dDA==","signature_status":"signed_v1","signed_at":"2026-07-05T10:57:46.239848Z","signed_message":"canonical_sha256_bytes"},"source_id":"2505.01150","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:cccfb90064ea48decc8b9b9784bc8a1ef92af57f727001b23da3178e237ddfb8","sha256:2a5f8c40a700539d0edfe0b873cdef2e6110af0797d86dc588ded370df62d7b7"],"state_sha256":"2182dd6bbdce8b40b4346718550bc5c434e310d238cbafb40b605c649231ac87"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"7cRstqOOuUDzvAln7qSarcrn4GS4fpuL2bnDkFmNSfVsKCji/uzJQQw04AmN7jxFdRhrJ28Pw7zQxpWnhMOcAw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-08T07:33:48.483508Z","bundle_sha256":"0c6dbe7ae528aec23a3e403eca95fa2b1a6c9833202d72628367c8254224e270"}}