From the Student Success edition of September 29, 2026
Accessible but not adopted: FGLI college students stay at shallow chatbot GenAI use
Harvard University (Hyungsik Kim) · Higher education / first-generation low-income student success · United States (primarily Boston and New York college students)
- Publisher
- arXiv
- Original publication
- Submitted to arXiv 28 September 2026 (cs.HC)
- Source retrieved
- 2026-09-30
What happened
Drawing on 61 interviews (15 long-form FGLI students, 3 non-FGLI students, 3 FGLI program directors, 40 intercepts), Kim finds that while most FGLI participants use LLMs, none of 55 FGLI participants reported meaningful use beyond chatbot interfaces (ChatGPT/Claude), with narrow academic/career use cases. Barriers include low perceived career value, under-estimated self-efficacy, unclear starting points, low peer exposure, and resource/time constraints. The paper derives four inferred design principles: demonstrate user-specific (esp. career) value; clarify competency needs; scaffold from fundamentals to fluency; and proactively address resource barriers. Principles were not separately experimentally tested.
Why it matters
Directly challenges seat-license or ‘access equals equity’ GenAI student-success strategies for first-generation and low-income undergraduates. Campuses expanding ChatGPT Edu or ambient tutors should fund career-relevant demos, peer modeling, scaffolded fluency pathways, and cost/time relief—or risk widening a depth gap even when tools are free.
Evidence and measured results
Qualitative thematic analysis of semi-structured and intercept interviews with FGLI students and program directors; adoption conceptualized as a four-mode depth gradient (chatbot → tool-augmented → agentic → programmatic). Cross-sectional U.S. East Coast sample; preprint.
Limitations and uncertainty
East Coast concentration; mostly freshmen/sophomores; cross-sectional; tools evolving monthly; design principles inferred from themes, not RCTs; single-author preprint.
Put this evidence to work
Lighthouse Advisory interpretation, grounded in this source. Enriched 2026-09-30; this does not change the original publication date. Each role follows the same checklist so takeaways compare across sources.
Sales
Role takeaway
- Customer problem
- Campus GenAI access is expanding while first-gen/low-income students remain at shallow chatbot-only use.
- Stakeholders
- student success
- FGLI/TRIO programs
- teaching & learning
- academic technology
- career services
- Discovery questions
- How do we measure GenAI adoption depth beyond logins?
- Which majors see the lowest perceived career relevance?
- What peer models exist for FGLI students?
- Value hypothesis
- Career-demonstrated, scaffolded GenAI literacy can deepen FGLI adoption beyond chatbots when resource barriers are actively reduced.
- Potential engagement
- 6–10 week FGLI co-designed GenAI fluency pilot with depth-of-use metrics and peer modeling.
- Unsupported claims
- Causal persistence/graduation lift
- Nationwide generalizability from East Coast interviews
- Validated DP1–DP4 intervention efficacy
Pre-sales engineering
Role takeaway
- Fit
- Campus LLM portals and literacy tooling for undergraduates with equity analytics.
- Architecture and integration
- AuthN → major-tagged starter packs → usage-mode telemetry → optional mentor queue; minimize token cost defaults.
- Prerequisites
- FGLI program partnership
- consent for equity analytics
- career-relevant example bank
- Constraints
- qualitative evidence only
- rapid tool churn
- subscription/token cost externalities
- Security
- Treat FGLI flags and GenAI logs as sensitive education records; purpose-limit; no vendor training on student chats without contract.
- Proof of value
- Share of FGLI users beyond chatbot mode
- Self-efficacy/value survey deltas
- Peer-session completion rates
Delivery
Role takeaway
- Work
- Recruit FGLI cohorts, run scaffolded labs, stand up peer models, monitor resource barriers.
- Dependencies
- FGLI advisors
- career services examples
- IT license/token budget
- Ownership
- Student success / FGLI program leadership with academic technology support.
- Skills and adoption
- Facilitators who can demystify tools for non-CS majors without deficit framing.
- Governance checkpoints
- Co-design review with FGLI directors
- mid-pilot barrier check
- equity dashboard privacy review
- Acceptance criteria
- Documented depth-mode shift for pilot cohort
- Resource-barrier mitigations published
- No mandatory advanced-tool requirement without scaffolds
- Risks
- Deficit labeling
- Time poverty blocking participation
- Overclaiming untested design principles
Implementation considerations
Lighthouse Advisory interpretation across the operating dimensions a public-sector buyer must settle before this evidence becomes a design. Each note answers the question under its heading for this specific source.
Architecture and integration
What must connect, and where does the AI sit in the workflow?
Pair institutional LLM access with onboarding that demonstrates major-specific value, competency maps, and progressive labs—not a single chatbot landing page. Instrument adoption depth (modes of use) rather than login counts. Prefer low-friction free tiers and campus-hosted options that reduce token/subscription anxiety for FGLI students.
Governance
Who approves, reviews and stays accountable for outcomes?
Equity dashboards should report depth-of-use and barrier themes by first-gen/Pell status, not only license uptake. Design principles are interpretive recommendations—require local co-design with FGLI programs before mandating advanced agentic tools.
Security and privacy
What data, permissions and controls need testing?
Interview study itself is low-risk research; campus interventions that track FGLI status with GenAI usage logs create sensitive education records requiring FERPA minimization, purpose limitation, and care against deficit framing in automated flags.
Accessibility and workforce
Who is affected, and what skills or accommodations follow?
Shows GenAI equity is an accessibility-of-fluency problem: FGLI students need scaffolded pathways, peer exposure, and relief from time/money constraints—not only licenses. Workforce implication: FGLI advisors and career services become co-owners of GenAI literacy, not just IT.
Procurement
What should contracts, pricing and exit terms secure?
RFPs should require adoption-depth metrics, major-specific onboarding, and low/no-cost student tiers. Do not accept seat counts as equity success. Prefer vendors supporting campus-hosted or capped-cost modes that address token anxiety.
Operating model
Which teams own the service once it runs?
Operate GenAI student success as a literacy and coaching program with FGLI partnership, peer modeling, and sequenced fluency—not a self-serve chatbot landing page. Track depth modes and barriers as routine ops metrics.
Publication history
- 2026-09-29Student Success · Issue 233 resources
Stable resource ID: kim-fgli-llm-access-adoption-20260928