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Direct linkElisabeth Bauer; Michael Sailer; Frank Niklas; Samuel Greiff; Sven Sarbu-Rothsching; Jan M. Zottmann; Jan Kiesewetter; Matthias Stadler; Martin R. Fischer; Tina Seidel; Detlef Urhahne; Maximilian Sailer; Frank Fischer – Journal of Computer Assisted Learning, 2025
Background: Artificial intelligence, particularly natural language processing (NLP), enables automating the formative assessment of written task solutions to provide adaptive feedback automatically. A laboratory study found that, compared with static feedback (an expert solution), adaptive feedback automated through artificial neural networks…
Descriptors: Artificial Intelligence, Feedback (Response), Computer Simulation, Natural Language Processing
Paul Fleckney; James Thompson; Paulo Vaz-Serra – Higher Education Research and Development, 2025
The higher education shift to remote learning due to mobility restrictions imposed during the COVID-19 pandemic highlighted the need to improve the student learning experience using more active learning models. One model is peer assessment. Despite positively impacting student learning, peer assessment uptake remains low, partly because designing…
Descriptors: College Students, COVID-19, Pandemics, Distance Education
Steffen Steinert; Lars Krupp; Karina E. Avila; Anke S. Janssen; Verena Ruf; David Dzsotjan; Christian De Schryver; Jakob Karolus; Stefan Ruzika; Karen Joisten; Paul Lukowicz; Jochen Kuhn; Norbert Wehn; Stefan Küchemann – Education and Information Technologies, 2025
As distance learning becomes increasingly important and artificial intelligence tools continue to advance, automated systems for individual learning have attracted significant attention. However, the scarcity of open-source online tools that are capable of providing personalized feedback has restricted the widespread implementation of…
Descriptors: Higher Education, Open Educational Resources, STEM Education, Feedback (Response)
