Fetching the paper…
Reading the bibliography…
Large language models (LLMs) have recently gained popularity.
The measurement of observer agreement for categorical data
Landis, J. R.; and Koch, G. G. 1977 · 1977
Earlier work this paper cites.
SUS-A quick and dirty usability scale
Brooke, J.; et al. 1996 · 1996
Earlier work this paper cites.
Scaling laws for neural language models
Kaplan, J.; McCandlish, S.; Henighan, T.; Brown, T. B.; Chess, B.; Child, R.; Gray, S.; Radford, A.; Wu, J.; and Amodei, D. 2020 · 2001
Earlier work this paper cites.
A statics concept inventory: Development and psychometric analysis
Steif, P. S.; and Dantzler, J. A. 2005 · 2005
Earlier work this paper cites.
Concepts of force and frictional force: the influence of preconceptions on learning across different levels
Sharma, S.; and Sharma, K. 2007 · 2007
Earlier work this paper cites.
The Aligned Rank Transform for Nonparametric Factorial Analyses Using Only Anova Procedures
Wobbrock, J. O.; Findlater, L.; Gergle, D.; and Higgins, J. J. 2011 · 2011
Earlier work this paper cites.
An inventory on rotational kinematics of a particle: unravelling misconceptions and pitfalls in reasoning
Mashood, K.; and Singh, V. A. 2012 · 2012
Earlier work this paper cites.
Qualitative HCI Research: Going behind the Scenes
Blandford, A.; Furniss, D.; and Makri, S. 2016 · 2016
Earlier work this paper cites.
Developing energy and momentum conceptual survey (EMCS) with four-tier diagnostic test items
Afif, N. F.; Nugraha, M. G.; and Samsudin, A. 2017 · 2017
Earlier work this paper cites.
Attention is all you need
Vaswani, A.; Shazeer, N.; Parmar, N.; Uszkoreit, J.; Jones, L.; Gomez, A. N.; Kaiser, Ł.; and Polosukhin, I. 2017 · 2017
Earlier work this paper cites.
Bert: Pre-training of deep bidirectional transformers for language understanding
Devlin, J.; Chang, M.-W.; Lee, K.; and Toutanova, K. 2018 · 2018
Earlier work this paper cites.
Improving language understanding by generative pre-training
Radford, A.; Narasimhan, K.; Salimans, T.; Sutskever, I.; et al. 2018 · 2018
Earlier work this paper cites.
A multi-method psychometric assessment of the affinity for technology interaction (ATI) scale
Lezhnina, O.; and Kismihók, G. 2020 · 2020
Cited alongside, same era.
Rediscovering the use of chatbots in education: A systematic literature review
Pérez, J. Q.; Daradoumis, T.; and Puig, J. M. M. 2020 · 2020
Cited alongside, same era.
Multiple-Choice Question Generation: Towards an Automated Assessment Framework
Raina, V.; and Gales, M. 2022 · 2022
Cited alongside, same era.
Wordcraft: story writing with large language models
Yuan, A.; Coenen, A.; Reif, E.; and Ippolito, D. 2022 · 2022
Cited alongside, same era.
Greaselm: Graph reasoning enhanced language models for question answering
Zhang, X.; Bosselut, A.; Yasunaga, M.; Ren, H.; Liang, P.; Manning, C. D.; and Leskovec, J. 2022 · 2022
Cited alongside, same era.
Physics task development of prospective physics teachers using ChatGPT
Küchemann, S.; Steinert, S.; Revenga, N.; Schweinberger, M.; Dinc, Y.; Avila, K. E.; and Kuhn, J. 2023 · 2023
Closest in time.
Interacting with educational chatbots: A systematic review
Kuhail, M. A.; Alturki, N.; Alramlawi, S.; and Alhejori, K. 2023 · 2023
Closest in time.
ScienceOlympiaden https://www.scienceolympiaden.de/
Leibniz-Institut für die Pädagogik der Naturwissenschaften und Mathematik. 2023 · 2023
Closest in time.
OpenAI. 2023 · 2023
Closest in time.
ChatGPT sets record for fastest-growing user base - analyst note https://www.reuters.com/technology/chatgpt-sets-record-fastest-growing-user-base-analyst-note-2023-02-01/
Reuters. 2023a · 2023
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
ChatGPT in physics education: A pilot study on easy-to-implement activities
Bitzenbauer, P. 2023 · 2023
Cited alongside, same era.
Palm-e: An embodied multimodal language model
Driess, D.; Xia, F.; Sajjadi, M. S.; Lynch, C.; Chowdhery, A.; Ichter, B.; Wahid, A.; Tompson, J.; Vuong, Q.; Yu, T.; et al. 2023 · 2023
Cited alongside, same era.
ChatGPT and the frustrated Socrates
Gregorcic, B.; and Pendrill, A.-M. 2023 · 2023
Cited alongside, same era.
Chatgpt for programming numerical methods
Kashefi, A.; and Mukerji, T. 2023 · 2023
Cited alongside, same era.
ChatGPT for good? On opportunities and challenges of large language models for education
Kasneci, E.; Seßler, K.; Küchemann, S.; Bannert, M.; Dementieva, D.; Fischer, F.; Gasser, U.; Groh, G.; Günnemann, S.; Hüllermeier, E.; et al. 2023 · 2023
Cited alongside, same era.
Educational data augmentation in physics education research using ChatGPT
Kieser, F.; Wulff, P.; Kuhn, J.; and Küchemann, S. 2023 · 2023
Cited alongside, same era.
Introducing ChatGPT https://openai.com/blog/chatgpt
OpenAI. 2022a
Cited in the paper.
Meta’s Twitter rival Threads surges to 100 million users faster than ChatGPT - analyst note https://www.reuters.com/technology/metas-twitter-rival-threads-hits-100-mln-users-record-five-days-2023-07-10/
Reuters. 2023b · 2023
Closest in time.
War of the chatbots: Bard, Bing Chat, ChatGPT, Ernie and beyond. The new AI gold rush and its impact on higher education
Rudolph, J.; Tan, S.; and Tan, S. 2023 · 2023
Closest in time.
Santos, R. P. d. 2023 · 2023
Closest in time.
Llama: Open and efficient foundation language models
Touvron, H.; Lavril, T.; Izacard, G.; Martinet, X.; Lachaux, M.-A.; Lacroix, T.; Rozière, B.; Goyal, N.; Hambro, E.; Azhar, F.; et al. 2023 · 2023
Closest in time.
Document-level machine translation with large language models
Wang, L.; Lyu, C.; Ji, T.; Zhang, Z.; Yu, D.; Shi, S.; and Tu, Z. 2023 · 2023
Closest in time.
UMUX-LITE: when there’s no time for the SUS
Lewis, J. R.; Utesch, B. S.; and Maher, D. E. 2013 · 2099
Closest in time.