Norwegian secondary teachers’ perceptions of artificial intelligence in summative assessments

Traditional summative assessment practices in Norwegian secondary schools have faced criticism for potential biases, inconsistencies in grading reliability, and heavy workload demands on teachers, prompting interest in whether artificial intelligence (AI) can enhance fairness, objectivity, and efficiency. This study examined secondary school teachers’ perceptions of AI as a tool for summative assessments and identified factors influencing their acceptance of its integration. A cross-sectional design targeted teachers from five socioeconomically and culturally diverse secondary schools in Oslo. Convenience sampling yielded 223 valid responses from paper-based questionnaires (response rate ~87%). Relationships among latent variables were tested using structural equation modelling: belief in AI for assessment enhancement, digital instructional efficacy, workload/time pressure, and preference for traditional teaching methods. Key findings revealed that digital instructional efficacy was positively associated with greater openness to AI-driven assessments (β = .38), supporting its role in promoting perceived efficiency and objectivity. Preference for traditional methods showed a moderate negative correlation with AI acceptance (β = −.18), while workload pressures had a negligible direct impact on AI beliefs (β = −.02) but indirectly reinforced reliance on conventional approaches. The results indicate a tension between progressive technological adoption and established pedagogical ideologies, suggesting that AI integration requires strategies that build teachers’ digital confidence while respecting traditional practices and managing workload to avoid entrenchment in familiar methods. Limitations include the Oslo-centric sample and reliance on self-reported data; future research should incorporate longitudinal designs, qualitative methods, and broader regional representation.

Authors

Institutions

Publication Details

Journal
International Journal of Assessment Tools in Education
Published
2026-02-15
DOI
https://doi.org/10.21449/ijate.1791004
Primary Topic
Teaching and Learning Programming
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Norwegian secondary teachers’ perceptions of artificial intelligence in summative assessments

Eyvind Elstad, Harald Eriksen
International Journal of Assessment Tools in Education
Teaching and Learning Programming
article

Norwegian secondary teachers’ perceptions of artificial intelligence in summative assessments

Eyvind Elstad, Harald Eriksen
article en

Abstract

Traditional summative assessment practices in Norwegian secondary schools have faced criticism for potential biases, inconsistencies in grading reliability, and heavy workload demands on teachers, prompting interest in whether artificial intelligence (AI) can enhance fairness, objectivity, and efficiency. This study examined secondary school teachers’ perceptions of AI as a tool for summative assessments and identified factors influencing their acceptance of its integration. A cross-sectional design targeted teachers from five socioeconomically and culturally diverse secondary schools in Oslo. Convenience sampling yielded 223 valid responses from paper-based questionnaires (response rate ~87%). Relationships among latent variables were tested using structural equation modelling: belief in AI for assessment enhancement, digital instructional efficacy, workload/time pressure, and preference for traditional teaching methods. Key findings revealed that digital instructional efficacy was positively associated with greater openness to AI-driven assessments (β = .38), supporting its role in promoting perceived efficiency and objectivity. Preference for traditional methods showed a moderate negative correlation with AI acceptance (β = −.18), while workload pressures had a negligible direct impact on AI beliefs (β = −.02) but indirectly reinforced reliance on conventional approaches. The results indicate a tension between progressive technological adoption and established pedagogical ideologies, suggesting that AI integration requires strategies that build teachers’ digital confidence while respecting traditional practices and managing workload to avoid entrenchment in familiar methods. Limitations include the Oslo-centric sample and reliance on self-reported data; future research should incorporate longitudinal designs, qualitative methods, and broader regional representation.

International Journal of Assessment Tools in Education(Advanced Online Publication)
University of Oslo (NO), Metropolitan University (BD)
Quality Education
Openalex Percentile: Top 61%
Teaching and Learning Programming
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.