KI in der Testentwicklung: zur Nützlichkeit von LLMs bei der Erstellung von Prüfungstexten

Literatur

Adesso, Gerardo (2023): „Towards the ultimate brain: Exploring scientific discovery with ChatGPT AI“. In: AI Magazine 44 (3), 328–342. DOI: https://doi.org/10.1002/aaai.12113.Suche in Google Scholar

Alkaissi, Hussam; McFarlane, Samy I. (2023): „Artificial hallucinations in ChatGPT: Implications in scientific writing“. In: Cureus 15 (2), e35179. DOI: https://doi.org/10.7759/cureus.35179.Suche in Google Scholar

Attali, Yigal; Runge, Andrew; LaFlair, Geoffrey T.; Yancey, Kevin; Goodwin, Sarah; Park, Yena; Davier, Alina A. von (2022): „The interactive reading task: Transformer-based automatic item generation“. In: Frontiers in Artificial Intelligence 5, 1–13. DOI: https://doi.org/10.3389/frai.2022.903077. 10.3389/frai.2022.903077Suche in Google Scholar

Benedetto, Luca; Gaudeau, Gabrielle; Caines, Andrew; Buttery, Paula (2025): „Assessing how accurately large language models encode and apply the common European framework of reference for languages“. In: Computers and Education: Artificial Intelligence 8, 1–24. DOI: https://doi.org/10.1016/j.caeai.2024.100353.Suche in Google Scholar

Bolender, Brad; Foster, Charles; Vispoel, Sara (2023): „The criticality of implementing principled design when using AI technologies in test development“. In: Language Assessment Quarterly 20 (4/5), 512–519. DOI: https://doi.org/10.1080/15434303.2023.2288266. Suche in Google Scholar

Brunfaut, Tineke (2021): „Assessing reading“. In: Fulcher, Glenn; Harding, Luke (Hrsg.): The Routledge handbook of language testing. London: Routledge, 254–267. DOI: https://doi.org/10.4324/9781003220756.Suche in Google Scholar

Chapelle, Carol A.; Lee, Hyewon (2021): „Understanding argument-based validity in language testing“. In: Chapelle, Caroline A.; Voss, Erik (Hrsg.): Validity argument in language testing: Case studies of validation research. Cambridge: Cambridge University Press, 19–44. DOI: https://doi.org/10.1017/9781108669849.004.Suche in Google Scholar

Chen, Jing; Sheehan, Kathleen M. (2015): „Analyzing and comparing reading stimulus materials across the ‘TOEFL’® Family of Assessments. ‘TOEFL iBT’® Research Report. TOEFL iBT-26. ETS Research Report no. RR-15-08“. In: ETS Research Report Series 1, 1–14. DOI: https://doi.org/10.1002/ets2.12055.Suche in Google Scholar

Drackert, Anastasia; Horbach, Andrea; Peters, Anja (2025): „How good are LLMs in generating input text for reading tasks in German as a foreign language?“. In: Annual Review of Applied Linguistics 45, 222–252. DOI: https://doi.org/10.1017/S0267190525000066.Suche in Google Scholar

Feng, Shangbin; Park, Chanyoung; Liu, Yuhan; Tsvetkov, Yulia (2023): „From pretraining data to language models to downstream tasks: Tracking the trails of political biases leading to unfair NLP models“. In: Rogers, Anna; Boyd-Graber, Jordan; Okazaki, Naoaki (Hrsg.): Proceedings of the 61st annual meeting of the Association for Computational Linguistics, Band 1. Toronto: Association for Computational Linguistics, 11737-11762. DOI: https://doi.org/10.48550/arXiv.2305.08283.Suche in Google Scholar

Green, Anthony; Hawkey, Roger (2011): „Re-fitting for a different purpose: A case study of item writer practices in adapting source texts for a test of academic reading“. In: Language Testing 29 (1), 109–129. DOI: https://doi.org/10.1177/0265532211413445.Suche in Google Scholar

Jeon, Eun Hee; Yamashita, Junko (2020): „Measuring L2 reading“. In: The Routledge handbook of second language acquisition and language testing, 265–274. DOI: https://doi.org/10.4324/9781351034784.Suche in Google Scholar

Norris, John; Drackert, Anastasia (2018): „Test review: TestDaF“. In: Language Testing 35 (1), 149–157. DOI: https://doi.org/10.1177/0265532217715848.Suche in Google Scholar

O’Sullivan, Barry (2023): „Reflections on the application and validation of technology in language testing“. In: Language Assessment Quarterly 20 (4–5), 501–511. DOI: https://doi.org/10.1080/15434303.2023.2291486.Suche in Google Scholar

Pugh, Debra; De Champlain, André; Gierl, Mark; Lai, Hollis; Touchie, Claire (2020): „Can automated item generation be used to develop high quality MCQs that assess application of knowledge?“. In: Research and Practice in Technology Enhanced Learning 15, Art. 12. DOI: https://doi.org/10.1186/s41039-020-00134-8. Suche in Google Scholar

Shin, Dongkwang; Lee, Jangho (2023): „Can ChatGPT make reading comprehension testing items on par with human experts?“. In: Language Learning & Technology 27 (3), 27–40. DOI: https://doi.org/10.64152/10125/73530.10.64152/10125/73530Suche in Google Scholar

Xia, Menglin; Kochmar, Ekaterina; Briscoe, Ted (2016): „Text readability assessment for second language learners“. In: Tetreault, Joel; Burstein, Jill; Leacock, Claudia; Yannakoudakis, Helen (Hrsg.): Proceedings of the 11th workshop on innovative use of NLP for building educational applications. San Diego, CA: Association for Computational Linguistics, 12–22. DOI: https://doi.org/10.18653/v1/W16-0502.Suche in Google Scholar

Xiao, Changrong; Xu, Xin Sean; Zhang, Kunpeng; Wang, Yufang; Xia, Lei (2023): „Evaluating reading comprehension exercises generated by LLMs: A showcase of ChatGPT in education applications“. In: Kochmar, Ekaterina; Burstein, Jill; Horbach, Andrea; Laarmann-Quante, Ronja; Madnani, Nitin; Tack, Anais; Yaneva, Victoria; Zheng, Yuan; Zesch, Torsten (Hrsg.): Proceedings of the 18th workshop on innovative use of NLP for building educational applications. Toronto: Association for Computional Linguistics, 610–625. DOI: https://doi.org/10.18653/v1/2023.bea-1.52.Suche in Google Scholar