TRANSFORMING PROFESSIONAL PRACTICE WITH CHATGPT: LEARNING AND INFORMATION PROCESSING
Bibliographic record
Abstract
Purpose: The main objective of the present study was to identify differences in how employed and non-employed students evaluate ChatGPT’s dual functions – information processing and tutoring. Design/methodology/approach: A Computer-Assisted Self-Interview (CASI) survey was conducted in the second quarter of 2024. After excluding non-users of ChatGPT, 449 valid responses were analyzed. Instrument reliability and factorability were verified. To assess the intensity of selected variables, a five-point Likert-type scale was applied. Because variables departed from normality, non-parametric tests (Mann-Whitney U) compared evaluations between employed and non-employed respondents. Findings: Respondents in both groups evaluated ChatGPT positively as a substitute for a traditional search engine, with no notable differences between employed and non-employed students. In contrast, non-employed students assessed ChatGPT’s tutoring role more favorably, which may reflect their greater reliance on digital tools for academic support. Overall, evaluations tended to be positive, although the variability in responses suggests differing levels of familiarity with or expectations toward the technology. Research limitations/implications: This study reflects one point in time, so future research should examine changes over longer periods. The analysis focused only on two main functions of ChatGPT – information processing and tutoring and on general use rather than specific academic tasks. Because the sample consisted solely of Polish students, the findings may not be fully applicable in other cultural contexts. Future studies should therefore involve more diverse populations and explore additional functions and learning situations. Practical implications: For students and early-career knowledge workers, conversational search with summarized answers can serve as the standard approach. Tutoring and guided support may be especially useful for those with more time for structured learning, such as non employed students. Universities and organizations should combine AI use with basic training in how to check information, create effective prompts, and evaluate results, while also providing clear source information to ensure that human judgment remains central. Social implications: Adjusting AI support to students’ time and workload can help reduce inequalities in learning. Teaching habits of verification – such as citing sources and signaling uncertainty – can lower the risks of overreliance, bias, and weakened critical thinking, while still allowing users to benefit from productivity gains. Originality/value: Introduces a two-function framework (interactive retrieval/processing vs. tutoring) linking HCIR-style information work with AI-supported learning, and provides empirical evidence that employment status does not shape evaluations of the search-substitution function but does differentiate evaluations of the tutoring function in a large sample of active users.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".