Creativity and Innovation in Civic Spaces Supported by Cognitive Flexibility When Learning with AI Chatbots in Smart Cities
Bibliographic record
Abstract
The purpose of this study is to advance conceptual understandings of the cognitive flexibility construct, in support of creativity and innovation in smart city civic spaces, employing the use of large language model artificial intelligence chatbots such as ChatGPT. Based on a review of the research and practice literature, this study formulates a conceptual framework for cognitive flexibility in support of creativity and innovation in AI environments, adaptable to smart cities. A research design is used that employs AI as a design material, in combination with a topical inquiry involving boundary setting and perspective taking, to co-pilot an exploration with ChatGPT-3.5/4. This study operationalizes the framework for applications to learning approaches, addressing flexibility and inclusivity in smart city spaces and regions. With the rapid evolving of chatbot technologies, ChatGPT-4 is used in the exploration of a speculative real-world urban example. This work is significant in that AI chatbots are explored for application in urban spaces involving creative ideation, iteration, engagement, and cognitive flexibility; future directions for exploration are identified pertaining to ethical and civil discourse in smart cities and learning cities, as well as the notion that AI chatbots and GPTs (generative pre-trained transformers) may become a zeitgeist for understanding and learning in smart cities.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 source (direct Gemma or distilled Codex), 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".