Students face AI-free tests for AI-shaped jobs—what did 23 interviews reveal?
Engineering students and recent graduates in India described using AI to prepare for closed placement screens, clear coursework and meet AI-first workplace expectations. The Chennai-heavy qualitative sample exposes a tension, not a national prevalence estimate.
The short version
- What happened
- Researchers interviewed 23 AI-using students and 2026 graduates from Indian engineering and computing programmes: 13 students and 10 graduates, with two women in the sample.
- Who it affects
- Students may spend scarce time mastering tests that poorly match AI-assisted work, while employers receive weaker signals about independent reasoning and responsible tool use. Clearer policies and mixed AI-free and AI-permitted assessment could make expectations visible.
- What remains unproven
- This is an unreviewed qualitative study of 23 AI-using participants, not a representative survey of Indian engineering education.
Keep in mind
This is an unreviewed qualitative study of 23 AI-using participants, not a representative survey of Indian engineering education.
The sample clustered in established institutions, mostly in Chennai, included only two women and excluded several less-resourced settings.
Accounts were self-reported; graduates recalled earlier college experiences, and Tamil-English translation may have lost nuance.
Faculty, administrators, recruiters and managers were not interviewed, and no funding or competing-interest statement is reported.
Source analysis · 1 linked source. Original source date: 21 October 2026.
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Students described a mismatch between selection and work
The study interviewed 23 current students and recent graduates between September and October 2026. Thirteen were still studying and 10 had graduated; two were women. Participants came from engineering and computing programmes spanning several institution types, but most studied in Chennai. Seventeen interviews were in English and six partly or mainly in Tamil, usually lasting 30 to 45 minutes.
Across a reflexive thematic analysis, participants said campus placement gates—CGPA cut-offs, aptitude tests, coding screens and interviews—usually expected unaided performance. Yet graduates and interns described workplaces where AI use was normal or pressed by tighter deadlines. Many therefore used AI as a coach to prepare for tests that did not allow it, while remaining uncertain which foundational skills employers actually expected them to retain.[1]
The interviews illuminate a problem; they do not count it
Participants also distinguished passing or 'clearing' coursework from learning. AI helped explain weak teaching and fill gaps, but it could also produce assignments, rehearse predictable exam answers or become the first response to a task. Faculty rules were often unstated, and students described learning acceptable use from peers. Some graduates worried that work they could not fully claim as their own left them less able to defend a project in an interview or solve a problem unaided.
These are self-reported accounts, interpreted by the researchers rather than frequency estimates. Purposive and referral recruitment excluded rural, less-resourced and regional-language-medium institutions; only two participants were women, and the team did not interview faculty, recruiters or managers. The first author attended one represented institution and works as an AI consultant, a position addressed through reflexive notes and co-author consultation. Broader surveys and trials of AI-permitted assessments would test how far the pattern travels.[1]
The distinction that matters
The three gates participants described
AI use changed across the path from coursework to employment.
Coursework
Use AI to explain, draft or clear assignments
Grades may mix learning with assistance
Placement
Prepare with AI, then sit mostly AI-free screens
Selection tests unaided performance
Work
Use AI under delivery pressure
Employers may reward assisted speed
Watch the background, then check the evidence
Illustrative photo · not a video frame
UNESCO · 8 min 30 sec · Background explainer
Teacher to Teacher: Is AI reshaping education for the better?
YouTube loads only when you choose; playback starts when you press play.
A conversation about how teachers can guide AI use while preserving pupils’ judgement. Watch for the difference between helping a learner think and supplying an answer.
Evidence check: Teachers’ perspectives provide context. They do not establish that a particular AI tool improves learning outcomes.
Published 2025-01-23. Source checked 14 October 2026. Check captions on the publisher’s video page.
Read the education evidencePhoto: Vitaly Gariev / Unsplash · Licence
Continue the story
Read next
Continue exploring engineering education: compare the finding and its evidence limits.
How are Australian engineering students actually using generative AI?
A peer-reviewed survey of 131 students at two Australian universities found broad but uneven use: coding was reported by 46.6% of one cohort and 7.1% of the other. Because the cohorts differed in size, year level and location, the study cannot identify which factor caused the gap.
Read the next reportExplore the full source and evidence record
What this means for people
- Students may spend scarce time mastering tests that poorly match AI-assisted work, while employers receive weaker signals about independent reasoning and responsible tool use. Clearer policies and mixed AI-free and AI-permitted assessment could make expectations visible.
Global context
The conflict between AI-free selection and AI-assisted work is not unique to India, but campus placements, institution hierarchies and access to paid models shape it locally. Evidence from other regions should not be assumed to transfer.
What the evidence does not yet show
- This is an unreviewed qualitative study of 23 AI-using participants, not a representative survey of Indian engineering education.
- The sample clustered in established institutions, mostly in Chennai, included only two women and excluded several less-resourced settings.
- Accounts were self-reported; graduates recalled earlier college experiences, and Tamil-English translation may have lost nuance.
- Faculty, administrators, recruiters and managers were not interviewed, and no funding or competing-interest statement is reported.
What to watch next
- Representative surveys across Indian regions and institution tiers
- Interviews with recruiters, faculty and managers
- AI-permitted build tasks paired with oral explanation
- Longitudinal evidence on graduates' skills, job access and workplace performance
Living evidence record
Impact record IAI-1KV21TE
Evidence stage
Studied
Confidence
Supported
Reporting basis
Source analysis
Independent or research support
Not yet
Record status
Monitoring
Last checked
22 October 2026
Source trail
1 direct source across 1 source type.
People impact
Documented in this record.
Uncertainty
Limits and next checks are explicit.
Stages describe the evidence available—not whether a technology is good or bad. See the public method.
Single-source reporting disclosure
This record analyses one direct source. It can establish what arXiv published or reported, but it is not independent corroboration of every performance claim or predicted outcome. The confidence label will change only when broader evidence is added.
Evidence trail
Sources used for this report
Links checked 22 October 2026
Research question, topics and regions
How engineering and computing students in India use generative AI across coursework, placement preparation and the transition into AI-shaped technical work
Topics: Engineering education · Campus placements · AI literacy · Assessment · Graduate work
Regions: India · Asia
This report is labelled source analysis. We summarise and analyse source material in our own words; company statements remain attributed claims until independently supported. Translated summaries preserve the meaning of the original source and link back to it. Read our editorial standards.
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