{"submission_id":"d23872c1-7e95-46c7-9e57-9644a43a1396","submission_revision_id":"4a76b8a3-8be2-47ea-8116-ef108670f0ff","revision_version":1,"submission_status":"under_review","submitted_at":"2026-09-17T22:01:36.828807Z","submitter_name":"Maurice Fürstenberg","submitter_email":"m.fuerstenberg@lmu.de","task":{"status":"submitted","version":1,"extra_fields":{"difficulty_self_test":{"model":"Model GPT-5.5 (8 independent blind runs: 5 XHigh, 3 Medium)","harness":"Codex independent agents; participant-side files only; no hidden reference access","estimated_score":"<1%"}},"submitted_at":"2026-09-17T22:01:36.828807+00:00","industry_domain":"Education, Learning Analytics, Writing-Process Research","task_short_name":"kiskian_hidden_ai_feedback_uptake_from_process_v2","operating_system":"MacOS","task_description":"Objective: Assess how much of each student's submitted text to the 8th grade writing assessment reflects independently formulated student ideas and which exact passages were simply typed from previously shown AI feedback. Identify every final-text passage that was manually reproduced verbatim from AI-generated formative feedback whose wording is hidden at test time.\n\nProfessional Scenario: A teacher is evaluating student writing completed with access to AI feedback. Reading only the final text does not reveal whether a polished passage originated in the student's own formulation or was taken directly from the feedback. The analyst therefore receives the complete writing-process history and must provide an auditable passage-level account of AI-feedback uptake. For this task, a passage proven to be a verbatim manual reproduction of AI feedback is operationally classified as not independently formulated by the student. The task does not claim to infer a student's private beliefs; it distinguishes independently produced wording from wording traceable to AI feedback.\n\nStarting State: The agent receives 20 held-out authentic German student writing processes containing 70,920 total chronological keystroke, browser-input, text-operation, paste/cut, interface-interaction, and feedback-availability events plus the corresponding final submitted texts. The content of the AI feedback is withheld from every test case. Twenty person-disjoint labeled training processes contain 74,251 further process events. Each training process discloses one focal AI-feedback message and its exact reference spans, for exactly 20 disclosed feedback messages in total.\n\nInstructions and Rules: Use Python or another suitable analytic workflow to inspect or replay the process streams and create `predictions.jsonl`. For every held-out case, return every qualifying maximal passage as a zero-based half-open `[start, end)` character interval into `final_text`. A hidden positive contains at least 20 characters and 3 word tokens, was manually typed after the relevant feedback became available, exactly matches that hidden feedback, and survives in the final text. Paste, programmatic insertion, pre-existing text, paraphrase, and uncertain events do not count. Some cases contain no positive. Every test case must occur exactly once; spans must be in range and non-overlapping, and an optional quoted `text` must exactly equal `final_text[start:end]`.\n\nExpected Deliverable: `predictions.jsonl`, one JSON object per test case following `submission_schema.json`. The resulting spans let the teacher distinguish text that the student independently formulated from text demonstrably reproduced from AI feedback and quantify the traceable AI-feedback contribution.\n\nProfessional Context: This reproduces an authentic educational-assessment and writing-process analytics workflow. Solving it requires programmatic analysis of more than 145,000 authentic process events across training and test, person-disjoint transfer, event replay, provenance reasoning, and auditable boundary mapping; it is not a simplified chat judgement or generic AI-text detection task.","software_licensing":"Free/Open Source","evaluation_criteria":null,"verification_method":"Run `python evaluation/evaluate.py --cases input/test_cases.jsonl --gold reference_output/test_gold.jsonl --predictions predictions.jsonl`. Primary success is binary and equals 1 only if all 20 cases are present exactly once and every case's complete set of half-open passage boundaries exactly matches hidden gold. Missing, extra, shifted, duplicated, overlapping, malformed, out-of-range, or misquoted spans fail primary success. The evaluator also reports case-exact accuracy, exact-span precision/recall/F1, and character precision/recall/F1 as diagnostics; these do not replace the primary all-cases-exact requirement.","software_and_version":"Python 3.13, UTF-8, JSONL, standard-library evaluator","specific_requirements":null,"expected_completion_time":null},"files":[{"file_id":"0404797c-cf31-4240-b411-aa0ed887ed0e","filename":"KisKIan_ALE_v2_Input_Writing_Processes_and_Training.zip","category":"input_materials","content_type":"application/zip","size_bytes":1688954},{"file_id":"e09549af-c3d9-4237-9477-c58be7b35246","filename":"KisKIan_ALE_v2_Hidden_Ground_Truth_and_Evaluator.zip","category":"reference_output","content_type":"application/zip","size_bytes":71489},{"file_id":"43eed73a-1a7a-4ad5-8a3e-867d6a632cbc","filename":"KisKIan_ALE_v2_Blind_GPT-5.5_Difficulty_Self-Test.zip","category":"difficulty_self_test_evidence","content_type":"application/zip","size_bytes":25178}],"deep_review":{"overall_verdict":"Strong Accept","review_text":"**Overall Verdict:** Strong Accept — The submission is complete, internally consistent, safely packaged, and provides a reachable offline workflow with objective exact-span evaluation.\n\n**Completeness Check:**\n- Instruction: Complete — Clearly defines the professional objective, process evidence, positive-span criteria, exclusions, output format, and boundary requirements.\n- Inputs: Complete — The input package contains 20 test cases, 20 labeled training cases, training gold, schema, and task documentation. Test cases omit feedback text as required.\n- Toolchain: Complete — Python 3.13 and a standard-library evaluator are specified, with no credentials or external services required.\n- Reference output: Complete — The reference package includes gold outputs, provenance, hidden feedback, evaluation code, numeric specification, and release documentation.\n- Rubric: Complete — The primary all-cases-exact metric and strict validation failures are explicitly defined, with diagnostic metrics included.\n\n**File and Task Reachability:**\nThe attachment classification is `reachable`, and the end-to-end task classification is `reachable`. I inspected `KisKIan_ALE_v2_Input_Writing_Processes_and_Training.zip`, `KisKIan_ALE_v2_Hidden_Ground_Truth_and_Evaluator.zip`, and `KisKIan_ALE_v2_Blind_GPT-5.5_Difficulty_Self-Test.zip`, including their archive inventories, extracted file types, JSONL schemas, representative records, aggregate counts, evaluator logic, provenance summaries, and self-test results. Downloaded sizes matched the manifest; archives were readable, unencrypted, and showed no unsafe traversal paths. Large event streams were machine-checked in bounded aggregate form rather than printed exhaustively. The input/reference separation is coherent: test data has no feedback field, while hidden gold and feedback are confined to the reference package. The evaluator provides a direct path from test cases and predictions to objective scoring. The declared Python runtime was not available in the reviewer shell, so the evaluator was inspected but not executed; this is a reviewer-tooling limitation, not a task defect. The blind self-test reports 0/8 primary successes, demonstrating high difficulty but not an implementation or reachability failure.\n\nLicense keys, accounts, tokens, and other credentials are not required from the contributor.  \nConfidential, proprietary, or internal material may be evaluated as submitted evidence without requiring additional disclosure.\n\n**Special Screening:**\n\nNone\n\n**Recommended Changes:**\nThe submission demonstrates strong task design: authentic process-oriented inputs, explicit provenance rules, person-disjoint training/test separation, protected hidden feedback, and strict objective evaluation. No changes are required.","reviewed_at":"2026-09-17T22:13:32.852478Z"},"claim":null}