Sima Caspari-Sadeghi

Norwegian version of this page Department of Education, ICT and Learning

Norwegian version of this page Position

Associate Professor

Contact

+4769608258

+4797353163

sima.caspari-sadeghi@hiof.no

Study place

Halden

Office nr.

G1-026

Academic interests

Artificial Intelligence in Research & Education
Human-Computer Interaction (HCI)
Mathematics Education (STEM)
Assessment, Evaluation & Measurement

Courses taught

PhD course: Artificial Intelligence in Research Methodology
PHDDS90323 Research Methods: Methodology Matters (Spring 2026)
PhD course: Artificial Intelligence in Education
PHDDS91423 Education in the Digital Society (Spring 2025)
PHDDS91423 Education in the Digital Society (Spring 2024)
Massive Open Online Course (MOOCs) on Digital Competence
Statistics (Master level)
Evidence-based Evaluation (PhD course)
Technology-enhanced Learning & Teaching (PhD course)

Course Development

Artificial Intelligence for Schools (Kunstig intelligens-MOOC)

Positions held

2023: Associate Professor of ICT & Learning (Information & Communication Technology) – Faculty member
2019–2025: Habilitation in Digital Education (Germany), Venia Legendi: Teaching & Learning with Digital Technologies
2014–2015: Postdoc Fellowship – Alexander von Humboldt (Germany)
2012–2016: Assistant Professor – Faculty member (Iran)
2008–2012: Ph.D

Editor

International Journal on the Social Consequences of Artificial Intelligence

Review (Research Funding & Journals)

Publications

Caspari-Sadeghi, S. (2026). Commentary: Artificial Intelligence, Science, and the Kill Chain

1. Caspari-Sadeghi, S. (2026). Adaptive Learning Systems: Leveraging Generative Artificial Intelligence (GenAI) for optimal adaptivity. In B. Wasson & C. E. Tømte (Eds.), Handbook of learning analytics and adaptive learning in compulsory education (pp. 21–34). Edward Elgar Publishing. https://doi.org/10.4337/9781035330676.00009

2. Caspari-Sadeghi, S. (2026). Artificial Intelligence (AI) Literacy for teacher educators: A holistic curriculum for capacity-building in higher education. Frontiers in Education, 11, 1745768. https://doi.org/10.3389/feduc.2026.1745768

3. Caspari-Sadeghi, S. (2026). Large Language Models (LLMs) in mathematics education: Practitioners as pedagogical prompt engineers. Journal of Higher Education Theory and Practice, 26(4), 16–26. https://doi.org/10.33423/7csntx42

4. Caspari-Sadeghi, S., Forster-Heinlein, B., Proell, M., Bredberg, J., & Jemai, K. (2026). Fostering evaluative judgement in mathematics: involving students in evaluation. International Journal of Mathematical Education in Science and Technology, 1–18. https://doi.org/10.1080/0020739X.2026.2625811

5. Caspari-Sadeghi, S., Bredberg, J., Forster-Heinlein, B., Proell, M., & Jemai, K. (2026). Teacher assessment literacy in mathematics: Enhancing professional judgement in higher education. European Journal of Mathematics and Science Education, 7(2), 71–86. https://doi.org/10.12973/ejmse.7.2.71

6. Caspari-Sadeghi, S. (2023). Learning assessment in the age of big data: Learning analytics in higher education. Cogent Education, 10 (1), 1–11. https://doi.org/10.1080/2331186X.2022.2162697

7. Caspari-Sadeghi, S., Mille, E., Epperlein, H., & Forster-Heinlein, B. (2023). Democratizing assessment for sustainable learning in mathematics instruction: Giving students agency in classroom research. IEEE Global Engineering Education (EDUCON). https://doi.org/10.1109/EDUCON54358.2023.10125218

8. Forster-Heinlein, B., Mille, E., Epperlein, H., & Caspari-Sadeghi, S. (2023). Three easy ways to foster self-assessment in STEM education. In Proceedings of the 2023 IEEE German Education Conference (GeCon). IEEE. https://doi.org/10.1109/GECon58119.2023.10295119

9. Mägdefrau, J., Köstler, V., & Caspari-Sadeghi, S. (2023). Action Research in der Hochschullehre: Methode und Befunde? Empirische Pädagogik, 37(3), 225–234. https://www.vep-landau.de/produkt/empirische-paedagogik-2023-37-3-digital/

10. Caspari-Sadeghi, S. (2022). Artificial intelligence in technology-enhanced assessment: A survey of machine learning. Journal of Educational Technology Systems, 51(3), 372–386. https://doi.org/10.1177/00472395221138791

11. Caspari-Sadeghi, S. (2022). Applying learning analytics in online environments: Measuring learners’ engagement unobtrusively. Frontiers in Education, 7, 840947. https://doi.org/10.3389/feduc.2022.840947

12. Caspari-Sadeghi, S., Mille, E., Epperlein, H., & Forster-Heinlein, B. (2022). Stimulating reflection through self-assessment: Certainty-based marking (CBM) in online mathematics learning. Mathematics Teaching Research Journal, 14(2), 145–157. https://files.eric.ed.gov/fulltext/EJ1350641.pdf

13. Caspari-Sadeghi, S. (2022). Teaching expertise in context: How to evaluate teachers' situated cognition? Higher Education Evaluation and Development, 16(1), 63–69. https://doi.org/10.1108/HEED-06-2022-081

14. Caspari-Sadeghi, S., Forster-Heinlein, B., Mägdefrau, J., & Bachl, L. (2021). Student-generated questions (SGQ): Developing mathematical competence through online assessment. International Journal for the Scholarship of Teaching and Learning, 15(1), Article 8. https://doi.org/10.20429/ijsotl.2021.150108

15. Caspari-Sadeghi, S., Forster-Heinlein, B., Mägdefrau, J., & Bachl, L. (2021). Sustainable e-assessment in mathematics instruction. In D. Kollosche (Ed.), Proceedings of the Eleventh International Mathematics Education and Society Conference. (Vol. 1, pp. 57–60). https://doi.org/10.5281/zenodo.5387708

16. Caspari-Sadeghi, S., & Koenig, J. (2018). On the adequacy of expert teachers: From practical convenience to psychological reality. International Journal of Higher Education, 7(5), 1–19. https://doi.org/10.5430/ijhe.v7n5p1

17. Sadeghi, S. (2014). High-stake test preparation courses: Washback in accountability contexts. Journal of Education and Human Development, 3(1), 17–26. https://jehd.thebrpi.org/vol-3-no-1-march-2014-abstract-29-jehd

18. Sadeghi, S., Ketabi, S., Tavakoli, M., & Sadeghi, M. (2012). Application of critical classroom discourse analysis (CCDA) in classroom interaction. English Language Teaching Journal, 5(1), 166–173. https://doi.org/10.5539/elt.v5n1p166

19. Tavakoli, M., & Sadeghi, S. (2011). An inquiry-based approach toward teacher professionalism: Trichotomous model of critical ethnography. Journal of Reflective Practice, 12(3), 361–373. https://doi.org/10.1080/14623943.2011.571867

20. Sadeghi, S., & Ketabi, S. (2009). From liberal ostrichism to transformative intellectuals: An alternative role for Iranian critical pedagogues. English Language Teacher Development and Education, 12(1), 52–60. https://www.elted.net/uploads/7/3/1/6/7316005/v12_6sedeghi-ketabi.pdf

21. Sadeghi, S. (2008). Critical pedagogy in an EFL teaching context: An ignis fatuus or an alternative approach? Journal for Critical Education Policy Studies, 6(1), 276–299. http://www.jceps.com/?pageID=article&articleID=121

Theses and Dissertations

1. Caspari-Sadeghi, S. (2025). Technology-enhanced (AI) and formative assessment. Habilitation thesis, University of Passau, Germany.

2. Sadeghi, S. (2012). Teacher professionalism in assessment in high-stake testing contexts. PhD dissertation, University of Isfahan, Iran.

Conferences

1. Caspari-Sadeghi, S. (2026). Artificial intelligence (AI) in scientific research. Keynote presentation at the Open PhD Seminar: The role of artificial intelligence (AI) in research methodology and data analysis, Østfold University of Applied Sciences, Norway.

2. Caspari-Sadeghi, S., Bredberg, J., Forster-Heinlein, B., Proell, M., & Jemai, K. (2026). Matematik: et samtalsämne. Oral presentation at the Swedish Society for Research on Mathematics Didactic (SMDF- Svensk förening för matematikdidaktisk forskning), Gothenburg, Sweden.

3. Caspari-Sadeghi, S., Bredberg, J., Forster-Heinlein, B., Proell, M., & Jemai, K. (2025). Sustainable assessment-for-learning in mathematics: Developing students’ self-regulated learning. Oral presentation at the 26th AEA-Europe Annual Conference, The Hague, Netherlands.

4. Caspari-Sadeghi, S. (2024). Integrating multimodal Generative Artificial Intelligence (GenAI) in adaptive instructional systems: Opportunities and challenges. Oral presentation at Udforsk: Artificial Intelligence in Nordic Education, University of Agder, Kristiansand, Norway.

5. Caspari-Sadeghi, S., & Mägdefrau, J. (2024). Diversity factor in teacher professional development: What worked, what didn’t, and why. Oral presentation at the EARLI SIG 11–13 Conference, University of Oxford, Oxford, UK.

6. Caspari-Sadeghi, S. (2024). Validating Artificial Intelligence-based scoring engines: What does the machine measure? Oral presentation at the NORA Annual Conference (Norwegian Artificial Intelligence Research Consortium), Kristiansand, Norway.

7. Caspari-Sadeghi, S. (2024). Eine Meta-Aktionsforschung: Evidenzbasierte Bewertung mehrerer longitudinaler Aktionsforschungsprojekte. Oral presentation at the Collaborative Action Research Network (CARN) Conference: Akteure der Aktionsforschung und ihre Methoden, Bielefeld University, Bielefeld, Germany.

8. Caspari-Sadeghi, S., Mille, E., Epperlein, H., & Forster-Heinlein, B. (2022). Confidence-based marking: Fostering self-regulated learning in online mathematics learning. Oral presentation at the Society for Industrial and Applied Mathematics (SIAM) Conference on Applied Mathematics Education, University of Pennsylvania, Philadelphia, U.S.A.

9. Caspari-Sadeghi, S. (2022). Technology-enhanced assessment of mathematical competence: How Machine Learning is changing the measurement paradigm. Oral presentation at the German Psychological Society (Deutsche Gesellschaft für Psychologie; DGPs) Conference, University of Hildesheim, Hildesheim, Germany.

10. Caspari-Sadeghi, S., & Mägdefrau, J. (2022). Multimedia-integrated assessment: Using digital media in assessment-for-learning (AfL). Oral presentation at the European University Association (EUA) EU Forum for Assessment, Evaluation, and Applied Measurement, Bilbao, Spain.

11. Caspari-Sadeghi, S., Sailer, M., & Jung, S. (2022). Digitally enhanced feedback. Oral presentation at the International Society for Exploring Teaching and Learning (ISETL) Conference, North Carolina State University, U.S.A.

12. Caspari-Sadeghi, S., & Mägdefrau, J. (2022). Data-informed decision-making in higher education: Evidence-based evaluation of digital learning. Oral presentation at the 9th Annual Assessment Conference, Drexel University, Philadelphia, U.S.A.

13. Caspari-Sadeghi, S. (2022). The future(s) of action research: How action research can survive the Artificial Intelligence and big data era? Oral presentation at the Collaborative Action Research Network (CARN) Conference: Action research: Past, present, future, University of Klagenfurt, Klagenfurt, Austria.

14. Caspari-Sadeghi, S. (2021). Digital storytelling as a technology-enhanced assessment technique. Oral presentation at the 20th AEA-Europe Annual Conference, Association for Educational Assessment-Europe, UK.

15. Caspari-Sadeghi, S. (2021). Artificial intelligence (AI) in mathematics assessment. Oral presentation at the Annual Conference of the Austrian-German Mathematics Society (ÖDV), Passau, Germany.

16. Caspari-Sadeghi, S. (2021). Promoting self-regulated learning via online self-assessment. Oral presentation at the U.S. Association for the Assessment of Learning in Higher Education, University of Indianapolis, Indianapolis, U.S.A.

17. Caspari-Sadeghi, S. (2021). Learning analytics: Assessing students’ engagement in online environments. Oral presentation at the 3rd EU Conference on Digital Transformation and Innovation in Education, University of Bari, Bari, Italy.

18. Caspari-Sadeghi, S. (2021). PeerWise: Effective digital assessment through peer feedback. Oral presentation at the 5th International Conference on Digital Technology in Education (ICDTE), Busan, South Korea.

19. Caspari-Sadeghi, S., Forster-Heinlein, B., Mägdefrau, J., & Bachl, L. (2021). Sustainable e-assessment in mathematics instruction. Oral presentation at the 11th International Conference on Mathematics Education, University of Klagenfurt, Klagenfurt, Austria.

20. Yufu, M., Sadeghi, S., Egbert, M., & Hirataka, F. (2013). Glossing, transliterating, and translating interactional data on languages other than English for international publication. Oral presentation at the 13th International Pragmatics Conference, New Delhi, India.

21. Sadeghi, S. (2008). Teachers as critical pedagogues. Oral presentation at the 13th International Conference on Teaching in Southeast Asia, Singapore.

Research groups

Research in Digital Education - RIDE

Caspari-Sadeghi, Sima (2026). Large Language Models in Mathematics Education: Practitioners as Pedagogical Prompt Engineers. Journal of Higher Education Theory and Practice (JHETP). ISSN 2158-3595. 26(4), p. 16–26. doi: 10.33423/7csntx42. Full text in Research Archive Show summary
Effective classroom integration of large language models (LLMs) requires domain-specific Prompt Engineering (PE) competence among teachers and students. Existing PE literature remains largely technical or cross-disciplinary, offering limited guidance for mathematics education, where LLMs continue to struggle with mathematical reasoning. This paper proposes a pedagogically grounded framework that positions educators as pedagogical prompt engineers who scaffold students’ interactions with LLMs through discipline-specific prompting practices. The framework provides structured classroom exemplars to enhance mathematical reasoning, conceptual understanding, and critical evaluation of AI-generated outputs. By fostering critical AI literacy and human-centered inquiry, it supports deeper, reflective, and ethical engagement with intelligent technologies.
Caspari-Sadeghi, Sima; Bredberg, Johan Per Ivar Bo; Forster-Heinlein, Brigitte; Proel, Moritz & Jemai, Khaled Ben Latief (2026). Developing Assessment Literacy in Mathematics Instructors: Enhancing Professional Judgement in Higher Education. European Journal of Mathematics and Science Education (EJMSE). 7(2), p. 71–86. doi: 10.12973/ejmse.7.2.71. Full text in Research Archive Show summary
Teacher assessment literacy has been a focal point in educational policy, practice, and research for decades. While extensive literature exists on assessment literacy among K-12 teachers, empirical studies exploring its development among higher education faculty remain limited. This study addresses this gap by investigating how university mathematics educators in Germany and Norway enhanced their professional judgement of student work through a discipline-specific, classroom-embedded, and participatory professional development initiative. Drawing on theoretical models of teacher assessment competency and situated professional judgement, this research offers insights into fostering assessment literacy within higher education contexts. Educators collaboratively designed and implemented various assessment-for-learning interventions—such as rubrics and exemplars—and engaged in systematic inquiry through focused observation, field notes, and questionnaires. To deepen their professional judgement, educators conducted structured analyses of student work using cognitive commentary and think-aloud protocols. Inter-rater reliability checks were used to ensure consistency in their judgements. Findings from a follow-up focused group showed that educators’ assessment competence is highly context-dependent and discipline-specific. Their judgements were shaped by ongoing compromise and negotiation of factors like personal beliefs, pedagogical content knowledge, i.e., their knowledge of curriculum, expected learning progression, and learners. These findings support previous research on the adaptive nature of assessment competence and highlight the need for tailored, evidence-based professional development opportunities in assessment for higher education faculty.
Caspari-Sadeghi, Sima (2026). Adaptive learning systems: leveraging generative artificial intelligence for optimal adaptivity. In Tømte, Cathrine Edelhard; Barman, Linda & Wasson, Barbara (Ed.), Handbook of Learning Analytics and Adaptive Learning in Schools. Edward Elgar Publishing. ISSN 9781035330669. p. 21–34. doi: 10.4337/9781035330676. Full text in Research Archive Show summary
This timely Handbook explores the use of emerging digital technologies in education, demonstrating how learning analytics, adaptive technologies and artificial intelligence can be implemented to achieve pedagogical goals. As digital technologies become integral to education, chapters discuss how the focus is shifting from whether to use these tools, to how they can best support teaching and learning. Contributors address concerns surrounding data privacy, transparency and practical use, emphasising the importance of professional development and collaboration among educators, leaders and stakeholders. Drawing on empirical insights, chapters highlight the potential of learning analytics and adaptive learning to personalise education and improve outcomes for all students. This insightful Handbook promotes thoughtful policy, robust infrastructure and a thorough understanding of the professional and ethical contexts in which these tools are deployed. The Handbook of Learning Analytics and Adaptive Learning in Schools is an essential resource for scholars and students interested in educational technology, particularly those exploring the impact of data mining and learning analytics. It is also highly beneficial for educators and policymakers looking to ensure these emerging tools are used purposefully and in alignment with curriculum goals.
Caspari-Sadeghi, Sima (2026). Artificial Intelligence literacy for teacher educators: a holistic curriculum for capacity-building in higher education. Frontiers in Education. 11, p. 1–11. doi: 10.3389/feduc.2026.1745768. Full text in Research Archive Show summary
The emerge of Generative Artificial Intelligence (GenAI) sparked diverse reactions within the educational sector, from enthusiasm to prohibition. The core question is no longer whether educators should use GenAI, but rather what skills, knowledge, and mindset they need to use AI effectively and responsibly. Educational AI literacy focuses on the competencies educators must develop to understand, utilize, and critically evaluate AI in teaching, learning, and assessment. However, fostering educational AI literacy requires more than simply identifying and defining necessary competencies; existing literature does not adequately address providing a comprehensive curriculum for developing AI literacy in educational contexts. Drawing on an integration of conceptual theory, empirical research, and the author's extensive experience in faculty development across diverse international contexts, this conceptual paper introduces a holistic, participatory, and inquiry-driven curriculum framework for developing AI literacy among teacher educators in higher education. The synergy of these three sources of knowledge ensures a theoretically grounded and practically relevant foundation for the framework's design. Developed through an iterative process of design, implementation, and evaluation, the framework offers structured guidance for creating, embedding, and assessing AI literacy initiatives responsive to the unique needs and institutional contexts of higher education settings. Rather than prescribing a fixed hierarchical sequence, the framework is intentionally designed as a flexible blueprint—an open-ended roadmap, where the curriculum's depth and breadth can be adapted to an institution's AI-readiness and its broader mission regarding educators’ expected AI proficiency or mastery. Additionally, the analytical study addresses potential implementation challenges, strategies to mitigate them, and implications for stakeholders.
Caspari-Sadeghi, Sima; Forster-Heinlein, Brigitte; Proell, Moritz; Bredberg, Johan Per Ivar Bo & Jemai, Khaled Ben Latief (2026). Fostering evaluative judgement in mathematics: involving students in evaluation. International Journal of Mathematical Education in Science and Technology. ISSN 0020-739X. p. 1–18. doi: 10.1080/0020739x.2026.2625811. Full text in Research Archive Show summary
A fundamental goal of higher education is to develop students’ life-long learning capability. There is growing evidence that classroom assessment practises impact students’ approaches to learning, academic outcomes and future learning. This study explores how fostering students’ evaluative judgement competence – the capability to judge the quality of work of self and others – can enhance mathematics learning through carefully designed assessments which are both sustainable and transferable to future learning and job-related contexts. Using interventions such as exemplars, rubrics, feedback, self-assessment and peer assessment, students were provided with learning opportunities to practise evaluative judgement by understanding, justifying and producing work based on pre-defined quality standards and criteria. Data were collected over two semesters from undergraduate mathematics courses in Norway and Germany, examining three aspects of learning outcomes: (a) cognitive outcomes (e.g. student-generated artefacts), (b) affective outcomes (students’ motivation and satisfaction) and (c) behavioural outcomes (e.g. engagement). The findings suggest that evaluative judgement interventions had a positive overall impact on academic performance. However, the effectiveness and extent of these interventions varied depending on factors such as students’ characteristics and assessment cultures. The results can inform future research and practises to cultivate Self-Regulated Leaning (SRL) and evaluative judgement in mathematics education.
Forster-Heinlein, Brigitte; Epperlein, Hella; Mille, Elena & Caspari-Sadeghi, Sima (2023). Three Easy Ways to Foster Self-assessment in STEM Education. IEEE Global Engineering Education Conference, EDUCON. ISSN 2165-9559. doi: 10.1109/GECon58119.2023.10295119. Full text in Research Archive
Caspari-Sadeghi, Sima (2023). Artificial Intelligence in Technology-enhanced Assessment: A Survey of Machine Learning. Journal of Educational Technology Systems. ISSN 0047-2395. doi: 10.1177/00472395221138791. Full text in Research Archive
Caspari-Sadeghi, Sima (2023). Learning assessment in the age of big data: Learning analytics in higher education. Cogent Education. doi: 10.1080/2331186X.2022.2162697. Full text in Research Archive
Caspari-Sadeghi, Sima; Mille, Elena; Epperlein, Hella & Forster-Heinlein, Brigitte (2023). Democratizing Assessment for Sustainable Learning in Mathematics Instruction: Giving Students Agency in Classroom Research. IEEE Global Engineering Education Conference, EDUCON. ISSN 2165-9559. doi: 10.1109/EDUCON54358.2023.10125218. Full text in Research Archive

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Caspari-Sadeghi, Sima (2026). Artificial intelligence (AI) in scientific research. doi: https:/www.hiof.no/forskning/satsingsomrader/det-digitale-samfunn/aktuelt/arrangementer/open-seminar-the-role-of-artificial-intelligence-i.html. Full text in Research Archive Show summary
The Role of Artificial Intelligence in the Research Processes – Data analysis and beyond.
Caspari-Sadeghi, Sima; Bredberg, Johan Per Ivar Bo & Jemai, Khaled Ben Latief (2026). Matematik: kollegiala samtal för att förbättra professionell bedömning i matematik. doi: http:/matematikdidaktik.org/index.php/valkommen-till-madif-15/. Full text in Research Archive Show summary
Denna presentation ger en översikt över ett internationellt samarbetsprojekt som syftar till att stärka professionell bedömning i matematik. Inom utbildning syftar professionell bedömning på användandet av kunskap, färdigheter, erfarenhet eller intuition för att uppfatta och utvärdera kvalitet, styrkor och svagheter i studenters arbeten. Bedömning är den grundläggande basen för lärares pedagogiska beslut och handlingar i klassrummet (Poskitt och Mitchell, 2012). Trots att det finns omfattande forskning om professionell bedömning bland lärare i grund- och gymnasieskolan, saknas det i stor utsträckning empiriska studier som undersöker hur universitetslärare i matematik bedömer studenters matematiska resonemang och prestationer. Med hjälp av teoretiska ramverk för lärares bedömningskompetens (Xu och Brown, 2016) samt bedömnings- och beslutsfattandeprocesser (Borko et al., 2008), adresserar denna studie denna forskningslucka genom: (a) ett deltagarbaserat kompetensutvecklingsinitiativ som främjar tillförlitlig och konsekvent bedömning genom modererade, strukturerade matematiska samtal mellan kollegor, samt (b) en undersökning av vilka utmaningar lärare möter när de bedömer studentarbeten i matematik. Genom att betona samarbete och dialog mellan matematiker främjar projektet kommunikativa praxis som kan stärka kvaliteten på professionell bedömning i matematikundervisningen.
Caspari-Sadeghi, Sima (2026). Artificial Intelligence, science, and the kill chain – The Faculty of Teacher Education and Languages – Høgskolen i Østfold. Full text in Research Archive Show summary
COMMENTARY. In this commentary, Dr. Sima Caspari-Sadeghi reflects on how AI is transforming both scientific research and modern warfare — and why the academic community can no longer afford to stay on the sidelines.
Caspari-Sadeghi, Sima (2025). Habilitation Thesis: Technology-enhanced (AI) & Formative Assessment. doi: https:/www.ub.uni-passau.de/. Full text in Research Archive
Caspari-Sadeghi, Sima; Bredberg, Johan Per Ivar Bo; Jemai, Khaled Ben Latief; Forster-Heinlein, Brigitte & Proell, Moritz (2025). Sustainable assessment-for-learning in mathematics: Developing students’ self-regulated learning. doi: https:/aea-europe.net/26th-annual-conference-of-the-association-for-educational-assessment-europe/. Full text in Research Archive
Caspari-Sadeghi, Sima (2024). Revolutionizing education with AI: Insights from Sima Caspari-Sadeghi – The Faculty of Teacher Education and Languages – Høgskolen i Østfold. [Internet]. Høgskole i Østfold. Full text in Research Archive Show summary
Associate Professor Sima Caspari-Sadeghi, one of the recent additions to Østfold University College, is breaking new ground in the field of AI and Education. Her research focuses on integrating AI with psychometrics to better understand and improve learning processes, drawing from over 16 years of experience in academia.
Caspari-Sadeghi, Sima (2024). Eine Meta Aktionsforschung: Evidenzbasierte Bewertung mehrerer, longitudinaler Aktionsforschungsprojekte. doi: https:/www.uni-bielefeld.de/fakultaeten/erziehungswissenschaft/weos/transfer/veranstaltungen-zum-jubil/carn-dach/programm/freitag/1.5_Caspari.pdf. Full text in Research Archive
Caspari-Sadeghi, Sima & Jutta, Mägdefrau (2024). Diversity Factors in Teacher Professional Development. doi: https:/www.earli.org/sig-11-13-conference-2024. Full text in Research Archive
Caspari-Sadeghi, Sima (2024). Validating AI-based Scoring Engines: What does the Machine Measure? doi: https:/www.nora.ai/nora-events/annual-conference-2024/program.html. Full text in Research Archive
Caspari-Sadeghi, Sima (2024). Integrating Multimodal GenAI in Adaptive Instructional Systems: Opportunities & Challenges. Udforsk:Nordic Conference on AI Research in the Education. doi: https:/www.uia.no/english/about-uia/faculty/humanities-and-education/events/course-conference-seminar/abstracts_udforsk_ai_24.pdf. Full text in Research Archive
Mägdefrau, Jutta; Köstler, Verena & Caspari-Sadeghi, Sima (2023). Action Research in der Hochschullehre. Methoden und Befunde. Empirische Pädagogik. ISSN 0931-5020. Full text in Research Archive

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Published June 1, 2023 1:16 PM - Last modified Sep. 23, 2026 6:52 PM