Unmasking Chatbots' Multiple Personalities: A Student-Faculty Collaboration
Bibliographic record
Abstract
Humans have long anthropomorphized non-human entities, attributing human characteristics to objects like cars, sports equipment, and dolls. This tendency has intensified with connected devices and generative AI tools that simulate human interactions, producing sophisticated, human-like responses. If AI tools were personified based on their interaction and communication styles, what personalities might they embody? How could they be visually represented? What pedagogical opportunities could they reveal? These questions initiated the first phase of a practice-led project exploring AI personifications through a creative, collaborative pedagogical approach involving design students and faculty members. During an undergraduate design course, the faculty-student team used several generative AI tools for a project and reflected on their interactions by identifying various personalities the AI embodied. Each personality was named, described, and visually represented using generative AI illustration tools. The team identified six AI personalities: the assistant, the angel, the erudite, the slacker, the bullshitter, and the stalker. This project aims to contribute to emerging discussions about AI integration in education by offering a creative approach to support students and instructors who are navigating rapidly evolving technological interactions. By anthropomorphizing AI interactions, the team sought to enhance understanding of human-AI dynamics and potentially help develop AI literacy skills. The team also explores and reflects on a pedagogical approach that emphasizes student and faculty collaboration, creating a shared learning environment through a creative knowledge-building activity. This article presents the first phase of the project, offering early findings, exploring potential educational implications, and presenting future research directions.
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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 | 0.012 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.015 | 0.005 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".