Datasets:

backtothehuman
/

anatomy-generative-prototyping

Error code:   StreamingRowsError
Exception:    CastError
Message:      Couldn't cast
sequence: int64
session_id: string
mode: string
analysis_eligible: bool
ts_server: int64
ts_client: int64
client_sequence: int64
type: string
generation_id: string
task_index: int64
task_key: string
payload: struct<mode: string, attempt_no: int64, task_order: string, terminal: string, state: string, user_ag (... 3032 chars omitted)
  child 0, mode: string
  child 1, attempt_no: int64
  child 2, task_order: string
  child 3, terminal: string
  child 4, state: string
  child 5, user_agent: string
  child 6, age_band: string
  child 7, gender: string
  child 8, ai_tools_freq: string
  child 9, status: string
  child 10, source_used: string
  child 11, prompt_version: string
  child 12, model: string
  child 13, attempts: list<item: struct<temperature: int64, ok: bool>>
      child 0, item: struct<temperature: int64, ok: bool>
          child 0, temperature: int64
          child 1, ok: bool
  child 14, slots: struct<audience: string, core_purpose: string, item: string, item_plural: string, field_1: string, f (... 76 chars omitted)
      child 0, audience: string
      child 1, core_purpose: string
      child 2, item: string
      child 3, item_plural: string
      child 4, field_1: string
      child 5, field_2: string
      child 6, field_3: string
      child 7, summary_stat: string
      child 8, domain_label: string
  child 15, brief_sha256: string
  child 16, demo_cached: bool
  child 17, ratings: struct<harbour: int64, coldstore: int64, mill: int64, auction: int64>
      
...
, modified: bool
  child 53, undo_depth_before: int64
  child 54, undo_depth_after: int64
  child 55, kind: string
  child 56, phase: string
  child 57, sha256: string
  child 58, artifact_path: string
  child 59, storage_sha256: string
  child 60, size_bytes: int64
  child 61, reason: string
  child 62, artifact_event_id: string
  child 63, task: string
  child 64, template: string
  child 65, at: string
  child 66, answers: list<item: int64>
      child 0, item: int64
  child 67, mean: double
  child 68, tag: string
  child 69, cls: string
  child 70, element_id: string
  child 71, path: string
  child 72, href: string
  child 73, interactive: bool
  child 74, started_at: double
  child 75, terminal_at: double
  child 76, committed_generations: int64
codes: list<item: struct<field: string, title: string, question: string, lead: string, options: list<item:  (... 90 chars omitted)
  child 0, item: struct<field: string, title: string, question: string, lead: string, options: list<item: struct<valu (... 78 chars omitted)
      child 0, field: string
      child 1, title: string
      child 2, question: string
      child 3, lead: string
      child 4, options: list<item: struct<value: string, text: string>>
          child 0, item: struct<value: string, text: string>
              child 0, value: string
              child 1, text: string
      child 5, notes: list<item: string>
          child 0, item: string
      child 6, howto: list<item: null>
          child 0, item: null
to
{'codes': List({'field': Value('string'), 'title': Value('string'), 'question': Value('string'), 'lead': Value('string'), 'options': List({'value': Value('string'), 'text': Value('string')}), 'notes': List(Value('string')), 'howto': List(Value('null'))})}
because column names don't match
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 147, in get_rows_or_raise
                  return get_rows(
                      dataset=dataset,
                  ...<4 lines>...
                      column_names=column_names,
                  )
                File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
                  return func(*args, **kwargs)
                File "/src/services/worker/src/worker/utils.py", line 127, in get_rows
                  rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
                File "/src/services/worker/src/worker/utils.py", line 483, in safe_iter
                  yield from ds.decode(False) if ds.features else ds
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2840, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2373, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2398, in _iter_arrow
                  for key, pa_table in self.ex_iterable._iter_arrow():
                                       ~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 536, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 419, in _iter_arrow
                  for key, pa_table in self.generate_tables_fn(**gen_kwags):
                                       ~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 343, in _generate_tables
                  self._cast_table(pa_table, json_field_paths=json_field_paths),
                  ~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
                  pa_table = table_cast(pa_table, self.info.features.arrow_schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2378, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2306, in cast_table_to_schema
                  raise CastError(
                  ...<3 lines>...
                  )
              datasets.table.CastError: Couldn't cast
              sequence: int64
              session_id: string
              mode: string
              analysis_eligible: bool
              ts_server: int64
              ts_client: int64
              client_sequence: int64
              type: string
              generation_id: string
              task_index: int64
              task_key: string
              payload: struct<mode: string, attempt_no: int64, task_order: string, terminal: string, state: string, user_ag (... 3032 chars omitted)
                child 0, mode: string
                child 1, attempt_no: int64
                child 2, task_order: string
                child 3, terminal: string
                child 4, state: string
                child 5, user_agent: string
                child 6, age_band: string
                child 7, gender: string
                child 8, ai_tools_freq: string
                child 9, status: string
                child 10, source_used: string
                child 11, prompt_version: string
                child 12, model: string
                child 13, attempts: list<item: struct<temperature: int64, ok: bool>>
                    child 0, item: struct<temperature: int64, ok: bool>
                        child 0, temperature: int64
                        child 1, ok: bool
                child 14, slots: struct<audience: string, core_purpose: string, item: string, item_plural: string, field_1: string, f (... 76 chars omitted)
                    child 0, audience: string
                    child 1, core_purpose: string
                    child 2, item: string
                    child 3, item_plural: string
                    child 4, field_1: string
                    child 5, field_2: string
                    child 6, field_3: string
                    child 7, summary_stat: string
                    child 8, domain_label: string
                child 15, brief_sha256: string
                child 16, demo_cached: bool
                child 17, ratings: struct<harbour: int64, coldstore: int64, mill: int64, auction: int64>
                    
              ...
              , modified: bool
                child 53, undo_depth_before: int64
                child 54, undo_depth_after: int64
                child 55, kind: string
                child 56, phase: string
                child 57, sha256: string
                child 58, artifact_path: string
                child 59, storage_sha256: string
                child 60, size_bytes: int64
                child 61, reason: string
                child 62, artifact_event_id: string
                child 63, task: string
                child 64, template: string
                child 65, at: string
                child 66, answers: list<item: int64>
                    child 0, item: int64
                child 67, mean: double
                child 68, tag: string
                child 69, cls: string
                child 70, element_id: string
                child 71, path: string
                child 72, href: string
                child 73, interactive: bool
                child 74, started_at: double
                child 75, terminal_at: double
                child 76, committed_generations: int64
              codes: list<item: struct<field: string, title: string, question: string, lead: string, options: list<item:  (... 90 chars omitted)
                child 0, item: struct<field: string, title: string, question: string, lead: string, options: list<item: struct<valu (... 78 chars omitted)
                    child 0, field: string
                    child 1, title: string
                    child 2, question: string
                    child 3, lead: string
                    child 4, options: list<item: struct<value: string, text: string>>
                        child 0, item: struct<value: string, text: string>
                            child 0, value: string
                            child 1, text: string
                    child 5, notes: list<item: string>
                        child 0, item: string
                    child 6, howto: list<item: null>
                        child 0, item: null
              to
              {'codes': List({'field': Value('string'), 'title': Value('string'), 'question': Value('string'), 'lead': Value('string'), 'options': List({'value': Value('string'), 'text': Value('string')}), 'notes': List(Value('string')), 'howto': List(Value('null'))})}
              because column names don't match

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The Anatomy of a Generative Prototyping Session, Study 1

Anonymous authors, CHI 2027 submission. The dataset accompanies a preregistered study in which 100 non-experts completed three ten-minute prototyping tasks with Mark, a generative prototyping environment: two from a blank page, in an unfamiliar and in a familiar domain, and one on an existing prototype. Every generation is dissected into the formulation of the prompt (abstraction and specificity), the extent of the generated change (changed nodes), the verification time, and the move that followed (progression or repair).

Preregistration (anonymized view-only): https://osf.io/8wh4x/?view_only=84f9f96334d44652b9f5ddee4bbf3230

Files

File	Rows	Contents
`participants.csv`	100	one row per session: task order, age band, gender, AI-tool use, the T1 instance and its familiarity rating, the T3 rating, the two domain-knowledge probes, the familiarity differential, whether the session qualifies for the H5 test, and the session length
`generations.csv`	1646	one row per committed generation of the three tasks: prompt text, attached references, abstraction, specificity, changed nodes, generation time, verification time, and the following move
`attempts.csv`	2140	every prompt sent, including the tutorial and the attempts that were aborted or failed validation ({'committed': 1973, 'aborted': 96, 'failed': 70})
`briefs.csv`	100	the T1 instance and its brief, and the T3 brief generated from the participant's own area, with its slots
`events/<session>.jsonl.gz`	100 files	the complete research log of each session
`snapshots/<session>.tar.gz`	100 files	the source snapshots of the prototype before and after each generation, as the tool stored them
`coding/`		the prompts of the three coding passes and the code definitions

The research log

One JSON object per line. type names the event (prompt_sent, generation_started, generation_terminal, preview_click, composition_started, history_rolled_back, window_blur, task_started, task_timeout, and so on), ts_server and ts_client are milliseconds from the session start, task_key is T1, T2 or T3, and payload carries the event's data. A generation is committed when its change reached the preview, aborted when the participant interrupted it, and failed when the generated source did not pass validation; after an aborted or failed attempt the preceding snapshot was restored.

Measures

abstraction: the level of the prototype at which the prompt operates, product, design_system, feature or component, assigned by the coding pipeline.
specificity: the extent to which a request constrains its realization, from 1 (the request states an aim and leaves the change open) to 5 (the request determines the change).
changed_nodes: the number of nodes added, removed or modified in the structural diff of the source snapshots before and after the generation.
verification_s: seconds from the completion of the generation to the participant's first composition-related interaction, with the time the window was out of focus removed.
repair and repair_type: whether the next move returned to the request that produced the generation, and how (restate, refine, revert).

Anonymization

Session identifiers are the study's own pseudonyms; Prolific identifiers never entered the logs. Participant and run identifiers and absolute timestamps were removed. Age is given in five-year bands; occupation and the free-text area of expertise were dropped, and the generated T3 brief carries the domain in the words the generator chose. Prompts are the participants' own words; a scan found no e-mail addresses or URLs in them.

License and citation

CC BY 4.0. Cite the CHI 2027 submission "The Anatomy of a Generative Prototyping Session"; the reference will be completed after the review.

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