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ARXIV:2602.10802 · EDUCATIONAL AI · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE
ARXIV:2602.10802EDUCATIONAL AISUBMITTED 02 APR · 02:30 UTCFRESHNESS STALEarXiv
Conceptual framework for integrating GenAI in higher education considering stakeholder perceptions and EU compliance.
Opportunity summary
Pain Conceptual framework for integrating GenAI in higher education considering stakeholder perceptions and EU compliance.
Evidence 0 refs | 0 sources | 17% coverage
Blocker Evidence unverified
Conceptual framework for integrating GenAI in higher education considering stakeholder perceptions and EU compliance. GenAI is expected to enhance cognitive systems by enabling personalized learning and streamlining educational services.
Many staff and students in higher education have adopted generative artificial intelligence (GenAI) tools in their work and study. GenAI is expected to enhance cognitive systems by enabling personalized learning and streamlining educational services.
ScienceToStartup currently rates this 3.0/10 on the public viability pass. The results reveal both shared and discipline-specific themes, including strong interest in programming support from GenAI and concerns over response quality, privacy, and academic…
Educational AI moved forward this cycle; last verified April 2026. Public score 3.0/10.
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Conceptual framework for integrating GenAI in higher education considering stakeholder perceptions and EU compliance.
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Paper Pack
10.48550/arXiv.2602.10802Conceptual framework for integrating GenAI in higher education considering stakeholder perceptions and EU compliance.
Abstract
Many staff and students in higher education have adopted generative artificial intelligence (GenAI) tools in their work and study. GenAI is expected to enhance cognitive systems by enabling personalized learning and streamlining educational services. However, stakeholders perceptions of GenAI in higher education remain divided, shaped by cultural, disciplinary, and institutional contexts. In addition, the EU AI Act requires universities to ensure regulatory compliance when deploying cognitive systems. These developments highlight the need for institutions to engage stakeholders and tailor GenAI integration to their needs while addressing concerns. This study investigates how GenAI is perceived within the disciplines of Information Technology and Electrical Engineering (ITEE). Using a mixed-method approach, we surveyed 61 staff and 37 students at the Faculty of ITEE, University of Oulu. The results reveal both shared and discipline-specific themes, including strong interest in programming support from GenAI and concerns over response quality, privacy, and academic integrity. Drawing from these insights, the study identifies a set of high-level requirements and proposes a conceptual framework for responsible GenAI integration. Disciplinary-specific requirements reinforce the importance of stakeholder engagement when integrating GenAI into higher education. The high-level requirements and the framework provide practical guidance for universities aiming to harness GenAI while addressing stakeholder concerns and ensuring regulatory compliance.
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Extraction status
Derived fallbackRead summaries are estimated from adjacent metadata, not verified extraction rows.
Proof status
unverified0 refs; 0 sources; 17% coverage.
What was readable
Derived fallback: Estimated from adjacent evidence; not verified from source.
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Dimensions overall score 3.0
PROBLEM
Conceptual framework for integrating GenAI in higher education considering stakeholder perceptions and EU compliance. GenAI is expected to enhance cognitive systems by enabling personalized learning and streamlining educational services.
METHOD
Many staff and students in higher education have adopted generative artificial intelligence (GenAI) tools in their work and study. GenAI is expected to enhance cognitive systems by enabling personalized learning and streamlining educational services.
RESULT
ScienceToStartup currently rates this 3.0/10 on the public viability pass. The results reveal both shared and discipline-specific themes, including strong interest in programming support from GenAI and concerns over response quality, privacy, and academic integrity.
WHY NOW
Educational AI moved forward this cycle; last verified April 2026. Public score 3.0/10.
Abstract-backed public claims while anchored extraction refreshes.
Conceptual framework for integrating GenAI in higher education considering stakeholder perceptions and EU compliance. GenAI is expected to enhance cognitive systems by enabling personalized learning and streamlining educational services.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Many staff and students in higher education have adopted generative artificial intelligence (GenAI) tools in their work and study. GenAI is expected to enhance cognitive systems by enabling personalized learning and streamlining educational services.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 3.0/10 on the public viability pass. The results reveal both shared and discipline-specific themes, including strong interest in programming support from GenAI and concerns over response quality, privacy, and academic integrity.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Educational AI moved forward this cycle; last verified April 2026. Public score 3.0/10.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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Conceptual framework for integrating GenAI in higher education considering stakeholder perceptions and EU compliance.
Segment
Educational AI
Adoption evidence
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Commercial read
3.0/10 public viability
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CITED BY
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missing
reason
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proof status
unverified
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next verification path
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Technical feasibility
partial
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Evidence
0 references, 0 sources, 17% evidence coverage.
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Buyer clarity
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Integration burden
missing
Current read
No public implementation surface observed.
Evidence
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Write integration checklist from prototype path and target workflow.
Capital intensity
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Classify regulatory flags before commercialization planning.
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Regulatory need unclassified.
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ARTIFACTS
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DEFENSIBILITY
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