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Imagining assessment futures through artificial intelligence in higher education teachers’ perspectives
KTH, School of Industrial Engineering and Management (ITM), Learning, Digital Learning.ORCID iD: 0000-0002-6854-785x
KTH.
2025 (English)In: Discover Education, E-ISSN 2731-5525, Vol. 4, no 1, article id 532Article in journal (Refereed) Published
Abstract [en]

This paper presents an abundantly discussed topic with a deeper and focused context: the practitioners’ perceptions of the real use of AI in higher education and its consequences. Based on three research questions about how teachers perceive the challenges and opportunities of AI, specifically Generative AI, this study delves into how teachers naturally incorporate AI and its impact on higher education assessments when they contemplate the assessment landscape in their context, their foreseen benefits and threats, and how it impacts their future assessment practices in the growing digital world. Data from eight interviews of teachers at bachelor’s and master’s levels in a Swedish Engineering University were analysed using reflexive thematic analysis methodology. The results revealed a spectrum of perceptions, including known warnings about uses of AI, particularly when it is employed to generate knowledge elements that are directly evaluated by grading criteria. More nuanced concerns, such as the risk of over-relying on language correction tools or ideation support, were highlighted. Such practices may inadvertently hinder the development of essential cognitive and communicative skills, including critical thinking, creativity, and academic writing. Thus, findings pointed to the need for thoughtful boundaries and informed practices, including specific checkpoints to guide the responsible and pedagogically sound integration of AI in assessment contexts. As such, these insights serve as a foundation for developing practical guidelines and checklists for AI use in real-world higher education assessment scenarios.

Place, publisher, year, edition, pages
Springer Nature , 2025. Vol. 4, no 1, article id 532
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:kth:diva-374739DOI: 10.1007/s44217-025-00987-5Scopus ID: 2-s2.0-105023535476OAI: oai:DiVA.org:kth-374739DiVA, id: diva2:2023689
Note

QC 20251221

Available from: 2025-12-20 Created: 2025-12-20 Last updated: 2025-12-21Bibliographically approved

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Karunaratne, Thashmee

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CiteExportLink to record
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Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf