Chatbot Memory: Uncovering How Mental Effort and Chabot Interactions Affect Short-Term Learning
Bibliographic record
Abstract
Developments in artificial intelligence (AI) are transforming everyday tasks, including accessing information, learning, and decision making. Generative AI is representative of these changes as it can generate content traditionally reserved for humans with increased efficiency and reduced effort. This includes technologies like ChatGPT and other tools that exploit large language models, typically taking the form of conversational agents (chatbots). These technologies can be useful for self-regulated learning as is the case for Web browsing. It is, however, unclear whether learning with chatbots may be efficient as opposed to other Web-based approaches given the reduced effort related to chatbot interactions. This study assessed how interacting with a chatbot may affect short-term learning and the role of mental effort. Memory performance was equivalent across participants who either interacted with a chatbot or browsed the Internet to find information for answering essay questions. Differences in self-reported workload were, however, found across conditions.
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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 | 0.001 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".