Collaborative and AI-aided Exam Question Generation using Wikidata in Education. Scharpf, P., Schubotz, M., Spitz, A., Greiner-Petter, A., & Gipp, B. In Proceedings of the 3rd Wikidata Workshop 2022 co-located with the 21st International Semantic Web Conference (ISWC2022), volume 3262, of CEUR Workshop Proceedings, Hanghzou, China, 2022. CEUR-WS.org. Core Rank A![Collaborative and AI-aided Exam Question Generation using Wikidata in Education [pdf]](https://bibbase.org/img/filetypes/pdf.svg "pdf")
Paper abstract bibtex Since the COVID-19 outbreak, the use of digital learning or education platforms has significantly increased. Teachers now digitally distribute homework and provide exercise questions. In both cases, teachers need to continuously develop novel and individual questions. This process can be very time-consuming and should be facilitated and accelerated both through exchange with other teachers and by using Artificial Intelligence (AI) capabilities. To address this need, we propose a multilingual Wikimedia framework that allows for collaborative worldwide teacher knowledge engineering and subsequent AI-aided question generation, test, and correction. As a proof of concept, we present \textgreater\textgreaterPhysWikiQuiz\textless\textless, a physics question generation and test engine. Our system (hosted by Wikimedia at https://physwikiquiz.wmflabs.org/) retrieves physics knowledge from the open community-curated database Wikidata. It can generate questions in different variations and verify answer values and units using a Computer Algebra System (CAS). We evaluate the performance on a public benchmark dataset at each stage of the system workflow. For an average formula with three variables, the system can generate and correct up to 300 questions for individual students based on a single formula concept name as input by the teacher.
@inproceedings{BibbaseScharpf22,
address = {Hanghzou, China},
series = {{CEUR} {Workshop} {Proceedings}},
title = {Collaborative and {AI}-aided {Exam} {Question} {Generation} using {Wikidata} in {Education}},
volume = {3262},
url = {https://ceur-ws.org/Vol-3262/paper13.pdf},
abstract = {Since the COVID-19 outbreak, the use of digital learning or education platforms has significantly increased. Teachers now digitally distribute homework and provide exercise questions. In both cases, teachers need to continuously develop novel and individual questions. This process can be very time-consuming and should be facilitated and accelerated both through exchange with other teachers and by using Artificial Intelligence (AI) capabilities. To address this need, we propose a multilingual Wikimedia framework that allows for collaborative worldwide teacher knowledge engineering and subsequent AI-aided question generation, test, and correction. As a proof of concept, we present {\textgreater}{\textgreater}PhysWikiQuiz{\textless}{\textless}, a physics question generation and test engine. Our system (hosted by Wikimedia at https://physwikiquiz.wmflabs.org/) retrieves physics knowledge from the open community-curated database Wikidata. It can generate questions in different variations and verify answer values and units using a Computer Algebra System (CAS). We evaluate the performance on a public benchmark dataset at each stage of the system workflow. For an average formula with three variables, the system can generate and correct up to 300 questions for individual students based on a single formula concept name as input by the teacher.},
language = {en},
booktitle = {Proceedings of the 3rd {Wikidata} {Workshop} 2022 co-located with the 21st {International} {Semantic} {Web} {Conference} ({ISWC2022})},
publisher = {CEUR-WS.org},
author = {Scharpf, Philipp and Schubotz, Moritz and Spitz, Andreas and Greiner-Petter, Andre and Gipp, Bela},
year = {2022},
note = {Core Rank A},
}
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