Computer programming a chatbot to improve social-communication skills in autistic children: A feasibility study
Bibliographic record
Abstract
Purpose A pilot study evaluated the feasibility of a curriculum that overtly teaches computer programming while covertly scaffolding social-communication skills for autistic children aged 8–12 years. Methods Participants were taught the Python programming language so they could program their own chatbots to greet a human user and discuss different topics, taking turns during the discussion, as though the chatbot were a human itself. The students were challenged with creating chatbots that pass the ‘Turing Test’, where a human evaluator would not be able to tell whether their chatbots were humans or computer programs. The curriculum included didactic instruction, peer-group discussion, homework and the chatbot project. Six autistic children participated in the six-session program. Feasibility was assessed using questionnaires and qualitative feedback. Results The curriculum is deemed feasible and desirable. There was no measurable change in social-communication skills immediately following the six-session program. Participants and their parents were highly interested in similar programs in the future, suggesting promising potential for further development and refinement. Conclusion A curriculum of programming a chatbot that also covertly scaffolds social communication is feasible for autistic children who are interested in computer programming.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".