Natural language generation (NLG) is a process within artificial intelligence where computer systems produce human-comprehensible language texts from information. English as a foreign language (EFL) students’ use of NLG tools might facilitate their idea generation, which is fundamental to creative writing. However, little is known about how EFL students interact with NLG tools to generate ideas. This study explores strategies adopted by EFL students when searching for ideas using NLG tools, evaluating ideas generated by NLG tools and selecting NLG tools for idea generation. Four Hong Kong secondary school students attended workshops where they learned to write stories comprising their own words and words generated by NLG tools. After the workshops, they answered questions to reflect on their writing experience with NLG tools. In a thematic analysis of the written reflections, we found students may have existing ideas when searching for ideas and evaluating ideas with NLG tools. Students showed some aversion to ideas generated by NLG tools and selected NLG tools that generated a greater quantity of ideas. The findings inform our understanding of EFL students’ concerns when using NLG tools for idea generation and can inform educators’ instruction to implement NLG tools for classroom creative writing.
BenderE. M.GebruT.McMillan-MajorA.ShmitchellS. (2021). On the dangers of stochastic parrots: Can language models be too big?Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency. New York, NY. pp. 610–623https://doi.org/10.1145/3442188.3445922
BlackS.GaoL.WangP.LeahyC.BidermanS. (2021). GPT neo: Large scale autoregressive language modeling with mesh-tensorflow(1.0. [Python]. EleutherAIhttps://doi.org/10.5281/zenodo.5297715
BrownT. B.MannB.RyderN.SubbiahM.KaplanJ.DhariwalP.NeelakantanA.ShyamP.SastryG.AskellA.AgarwalS.Herbert-VossA.KruegerG.HenighanT.ChildR.RameshA.ZieglerD. M.WuJ.WinterC.AmodeiD. (2020). Language models are few-shot learners. (arXiv:2005.14165). arXivhttps://doi.org/10.48550/arXiv.2005.14165
6.
ButterfussR.RoscoeR. D.AllenL. K.McCarthyK. S.McNamaraD. S. (2022). Strategy uptake in writing pal: Adaptive feedback and instruction. Journal of Educational Computing Research, 60(3), 696–721. https://doi.org/10.1177/07356331211045304
7.
CalderwoodA.QiuV.GeroK. I.ChiltonL. B. (2018). How novelists use generative language models: An exploratory user study. IUI ’20: Proceedings of the 25th International Conference on Intelligent User Interfaces. Cagliari, Italy: ACM IUI 2020.
8.
ClarkE.RossA. S.TanC.JiY.SmithN. A. (2018). Creative writing with a machine in the loop: Case studies on slogans and stories. 23rd International Conference on Intelligent User Interfaces, Sydney, Australia, 329–340. https://doi.org/10.1145/3172944.3172983
DangH.MeckeL.LehmannF.GollerS.BuschekD. (2022). How to prompt? Opportunities and challenges of zero-and few-shot learning for human-AI interaction in creative applications of generative models. (arXiv:2209.01390). arXivhttps://arxiv.org/abs/2209.01390
11.
DawsonP. (2005). Creative writing and the new humanities. Routledge.
12.
DevlinJ.ChangM.-W.LeeK.ToutanovaK. (2019). BERT: Pre-training of deep bidirectional transformers for language understanding. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 1(Long and Short Papers), Minneapolis, MN, 4171–4186. https://doi.org/10.18653/v1/N19-1423
13.
FlowerL.HayesJ. R. (1981). A cognitive process theory of writing. College Composition and Communication, 32(4), 365–387. https://doi.org/10.2307/356600
14.
GattA.KrahmerE. (2018). Survey of the state of the art in natural language generation: Core tasks, applications and evaluation. Journal of Artificial Intelligence Research, 61, 65–170. https://doi.org/10.1613/jair.5477
15.
GayedJ. M.CarlonM. K. J.OriolaA. M.CrossJ. S. (2022). Exploring an AI-based writing assistant’s impact on English language learners. Computers and Education: Artificial Intelligence, 3, 100055. https://doi.org/10.1016/j.caeai.2022.100055
16.
GeroK. I.ChiltonL. B. (2019). Metaphoria: An algorithmic companion for metaphor creation. Proceedings of the 2019 CHI conference on human factors in computing systems, Glasgow, UK, (1–12). https://doi.org/10.1145/3290605.3300526
17.
GrahamS.CapizziA.HarrisK. R.HebertM.MorphyP. (2014). Teaching writing to middle school students: A national survey. Reading and Writing, 27(6), 1015–1042. https://doi.org/10.1007/s11145-013-9495-7
18.
GrahamS.PerinD. (2007). Writing next: Effective strategies to improve writing of adolescents in middle and high schools. A report to carnegie corporation of New York: Alliance for Excellent Education.
19.
HitsuwariJ.UedaY.YunW.NomuraM. (2023). Does human–AI collaboration lead to more creative art? Aesthetic evaluation of human-made and AI-generated haiku poetry. Computers in Human Behavior, 139, 107502. https://doi.org/10.1016/j.chb.2022.107502
KangasharjuA.IlomäkiL.LakkalaM.ToomA. (2022). Lower secondary students’ poetry writing with the AI-based poetry machine. Computers and Education: Artificial Intelligence, 3, 100048. https://doi.org/10.1016/j.caeai.2022.100048
22.
KaufmanJ. C.BeghettoR. A. (2009). Beyond big and little: The four C model of creativity. Review of General Psychology, 13(1), 1–12. https://doi.org/10.1037/a0013688
23.
KelloggR. T. (1999). The psychology of writing. Oxford University Press.
KimJ.LeeH.ChoY. H. (2022). Learning design to support student-AI collaboration: Perspectives of leading teachers for AI in education. Education and Information Technologies, 27(2022), 6069–6104. https://doi.org/10.1007/s10639-021-10831-6
26.
LeeM.LiangP.YangQ. (2022). CoAuthor: Designing a human-AI collaborative writing dataset for exploring language model capabilitiesProceedings of the 2022 CHI conference on human factors in computing systems, New Orleans, LA, (1–19). https://doi.org/10.1145/3491102.3502030
LinM. P.-C.ChangD. (2020). Enhancing post-secondary writers’ writing skills with a chatbot: A mixed-method classroom study. Journal of Educational Technology and Society, 23(1), 78–92. https://www.jstor.org/stable/26915408
29.
LiuP.YuanW.FuJ.JiangZ.HayashiH.NeubigG. (2021). Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing. (arXiv:2107.13586). arXivhttps://arxiv.org/abs/2107.13586
30.
MoonmaJ.KaweeraC. (2021). Collaborative writing in EFL classroom: Comparison on group, pair, and individual writing activities in argumentative tasks. Asian Journal of Education and Training, 7(3), 179–188. https://eric.ed.gov/?id=EJ1315594
RiniT. A.CahyantoB. (2020). Supporting elementary students creative writing skill with assessment as learning. Proceedings of the 2nd Early Childhood and Primary Childhood Education, Malang, Indonesia, (pp. 51–57). (ECPE 2020)https://doi.org/10.2991/assehr.k.201112.010
35.
Salas-PilcoS. Z. (2020). The impact of AI and robotics on physical, social-emotional and intellectual learning outcomes: An integrated analytical framework. British Journal of Educational Technology, 51(5), 1808–1825. https://doi.org/10.1111/bjet.12984
SeeA.PappuA.SaxenaR.YerukolaA.ManningC. D. (2019). Do massively pretrained language models make better storytellers? (arXiv:1909.10705). arXivhttps://doi.org/10.48550/arXiv.1909.10705
ShehadehA. (2011). Effects and student perceptions of collaborative writing in L2. Journal of Second Language Writing, 20(4), 286–305. https://doi.org/10.1016/j.jslw.2011.05.010
40.
SinghN.BernalG.SavchenkoD.GlassmanE. L. (2022). Where to hide a stolen elephant: Leaps in creative writing with multimodal machine intelligence. ACM Transactions on Computer-Human Interaction, 3511599. https://doi.org/10.1145/3511599
41.
StorchN. (2011). Collaborative writing in L2 contexts: Processes, outcomes, and future directions. Annual Review of Applied Linguistics, 31, 275–288. https://doi.org/10.1017/S0267190511000079
42.
TokŞ.KandemirA. (2015). Effects of creative writing activities on students’ achievement in writing, writing dispositions and attitude to English. Procedia—Social and Behavioral Sciences, 174, 1635–1642. https://doi.org/10.1016/j.sbspro.2015.01.815
VassE. (2007). Exploring processes of collaborative creativity—the role of emotions in children’s joint creative writing. Thinking Skills and Creativity, 2(2), 107–117. https://doi.org/10.1016/j.tsc.2007.06.001
46.
VassE.LittletonK.MiellD.JonesA. (2008). The discourse of collaborative creative writing: Peer collaboration as a context for mutual inspiration. Thinking Skills and Creativity, 3(3), 192–202. https://doi.org/10.1016/j.tsc.2008.09.001
WhiteJ.FuQ.HaysS.SandbornM.OleaC.GilbertH.ElnasharA.Spencer-SmithJ.SchmidtD. C. (2023). A prompt pattern catalog to enhance prompt engineering with ChatGPT. (arXiv:2302.11382). arXivhttps://arxiv.org/abs/2302.11382
49.
YangD.ZhouY.ZhangZ.LiT. J.-J. (2022). AI as an active writer: Interaction strategies with generated text in human-AI collaborative fiction writing. Joint Proceedings of the ACM IUI Workshops, Helsinki, Finland, 2022, 10. CEUR-WS Team.
50.
ZamelV. (1982). Writing: The process of discovering meaning. TESOL Quarterly, 16(2), 195–209. https://doi.org/10.2307/3586792
51.
ZhaiX.ChuX.ChaiC. S.JongM. S. Y.IstenicA.SpectorM.LiuJ.-B.YuanJ.LiY. (2021). A review of artificial intelligence (AI) in education from 2010 to 2020. Complexity, 2021, 1–18. https://doi.org/10.1155/2021/8812542
Supplementary Material
Please find the following supplemental material available below.
For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.
For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.
