Early Adoption of AI and Digital Communication Tools by MBA Students: Perceptions, Motivations, and Concerns
Bibliographic record
Abstract
As digital communication and artificial intelligence (AI) become increasingly embedded in higher education, understanding how professional graduate students plan to integrate these tools is essential. This study examines the early perceptions, intentions, and concerns of newly admitted MBA students regarding their use of communication technologies and AI tools during their first semester. Using an online questionnaire administered in weeks two and three of a mandatory managerial analytics course, data were collected from 45 respondents at a Canadian university. Results show that students rely on a mix of traditional and contemporary communication platforms, with email and WhatsApp being equally dominant, while in-person meetings remain surprisingly common. AI usage is already widespread, with 98% of students using ChatGPT and substantial proportions using Copilot and Gemini. Students primarily expect to use AI for writing-related tasks such as summarization, content structuring, brainstorming, and proofreading, motivated largely by a desire to save time and improve work quality. Although 93% anticipate that AI will enhance the quality of their academic output, they simultaneously express concerns regarding accuracy, originality, transparency, and privacy. Students perceive AI as moderately helpful for research, creativity, and problem solving but see limited benefits for critical thinking or team-based skills. Overall, the findings reveal a cohort eager yet cautious in adopting AI, highlighting the need for institutions to develop clear guidance and pedagogical strategies to support responsible and effective use of these tools as students progress through their program.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| 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".