MétaCan
Menu
Back to cohort
Record W4389404163 · doi:10.61838/hn.1.1.10

The Impact of Doing Assignments with Chatbots on The Students’ Working Memory

2023· article· en· W4389404163 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueHealth Nexus · 2023
Typearticle
Languageen
FieldComputer Science
TopicAI in Service Interactions
Canadian institutionsnot available
Fundersnot available
KeywordsWorking memoryChatbotCognitive loadCognitionPsychologyCognitive psychologyDifferential effectsDevelopmental psychologyComputer scienceArtificial intelligenceMedicine

Abstract

fetched live from OpenAlex

This study aimed to investigates the effects of chatbot usage on working memory in students who do their assignments with chatbots. The research employed a Single-Subject AB design involving three participants, with each phase consisting of four measurements. Remarkably, the study revealed diverse outcomes: one participant exhibited no significant change in working memory, another showed a decrease, and the third experienced a gradual increase. These varied results suggest that chatbots can have differential impacts on working memory, potentially explained by cognitive load theory. This theory emphasizes the importance of optimizing technology use in learning environments to support working memory functions. The study's findings indicate that chatbots, as an educational tool, can have complex and varying effects on students' cognitive abilities, particularly in terms of working memory.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.711
Threshold uncertainty score0.476

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.053
GPT teacher head0.379
Teacher spread0.325 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it