Few advancements in Artificial Intelligence (AI) have seemed to be quite as captivating for the public as was the release of ChatGPT by OpenAI. This chatbot has been able to provide remarkably accurate answers to factual questions, write workable code in a variety of programming languages, and generate human-like text passages on any number of topics. Because, at the time of writing, this technology is so new, there is a need to understand the capabilities and limitations of the system. This includes understanding the nature of collaborative and iterative writing. This paper reviews an attempt to write a conference proceedings paper on the ways in which to use ChatGPT to study human-AI interaction. Evaluation of output and the iterative processes required shows the strengths of ChatGPT’s context awareness, while highlighting its limitations in attempts to provide academic references. Additionally, metrics of passage similarity to prior passages is assessed.
BeggiatoM.PereiraM.PetzoldtT.KremsJ. (2015). Learning and development of trust, acceptance and the mental model of ACC. A longitudinal on-road study. Transportation Research Part F: Traffic Psychology and Behaviour, 35, 75–84. https://doi.org/10.1016/j.trf.2015.10.005
2.
BrandtzaegP. B.SkjuveM.FølstadA. (2022). My AI Friend: How Users of a Social Chatbot Understand Their Human–AI Friendship. Human Communication Research, 48(3), 404–429. https://doi.org/10.1093/hcr/hqac008
3.
BrownT. B.MannB.RyderN.SubbiahM.KaplanJ.DhariwalP.NeelakantanA.ShyamP.SastryG.AskellA.AgarwalS.Herbert-VossA.KruegerG.HenighanT. (2020). Language Models are Few-Shot Learners. Advances in Neural Information Processing Systems, 33.
de VisserE. J.PeetersM. M. M.JungM. F.KohnS.ShawT. H.PakR.NeerincxM. A. (2020). Towards a Theory of Longitudinal Trust Calibration in Human–Robot Teams. International Journal of Social Robotics, 12(2), 459–478. https://doi.org/10.1007/s12369-019-00596-x
6.
DengJ.LinY. (2023). The Benefits and Challenges of ChatGPT: An Overview. Frontiers in Computing and Intelligent Systems, 2(2), 81–83. https://doi.org/10.54097/fcis.v2i2.4465
EndsleyM. R. (2017). Autonomous Driving Systems: A Preliminary Naturalistic Study of the Tesla Model S. Journal of Cognitive Engineering and Decision Making, 11(3), 225–238. https://doi.org/10.1177/1555343417695197
9.
FischingerD.EinramhofP.PapoutsakisK.WohlkingerW.MayerP.PanekP.HofmannS.KoertnerT.WeissA.ArgyrosA.VinczeM. (2016). Hobbit, a care robot supporting independent living at home: First prototype and lessons learned. Robotics and Autonomous Systems, 75, 60–78. https://doi.org/10.1016/j.robot.2014.09.029
10.
GuoB.ZhangX.WangZ.JiangM.NieJ.DingY.YueJ.WuY. (2023). How Close is ChatGPT to Human Experts? Comparison Corpus, Evaluation, and Detection (arXiv:2301.07597). arXiv. http://arxiv.org/abs/2301.07597
11.
HaqueM. U.DharmadasaI.SwornaZ. T.RajapakseR. N.AhmadH. (2022). “I think this is the most disruptive technology”: Exploring Sentiments of ChatGPT Early Adopters using Twitter Data (arXiv:2212.05856). arXiv. http://arxiv.org/abs/2212.05856
12.
KingM. R. & chatGPT. (2023). A Conversation on Artificial Intelligence, Chatbots, and Plagiarism in Higher Education. Cellular and Molecular Bioengineering, 16(1), 1–2. https://doi.org/10.1007/s12195-022-00754-8
13.
KoayK. L.SyrdalD. S.WaltersM. L.DautenhahnK. (2007). Living with Robots: Investigating the Habituation Effect in Participants’ Preferences During a Longitudinal Human-Robot Interaction Study. RO- MAN 2007 - The 16th IEEE International Symposium on Robot and Human Interactive Communication, 564–569. https://doi.org/10.1109/ROMAN.2007.4415149
14.
KusnerM. J.SunY.KolkinN. I.WeinbergerK. Q. (2015). From Word Embeddings To Document Distances. Proceedings of the 32nd International Conference on Machine Learning.
15.
LiB.CaoH.LiuK.XiaJ.SunY.PengW.XieY.GuoC.LiuX.WenF.ZhangF.ShanG.ZhangL. (2022). Associations of long- term ambient air pollution and traffic-related pollution with blood pressure and hypertension defined by the different guidelines worldwide: The CHCN-BTH study. Environmental Science and Pollution Research, 29(42), 63057–63070. https://doi.org/10.1007/s11356-022-20227-9
16.
LiJ.MonroeW.RitterA.GalleyM.GaoJ.JurafskyD. (2016). Deep Reinforcement Learning for Dialogue Generation (arXiv:1606.01541). arXiv. http://arxiv.org/abs/1606.01541
17.
MirskyY.LeeW. (2022). The Creation and Detection of Deepfakes: A Survey. ACM Computing Surveys, 54(1), 1–41. https://doi.org/10.1145/3425780
18.
PentinaI.HancockT.XieT. (2023). Exploring relationship development with social chatbots: A mixed-method study of replika. Computers in Human Behavior, 140, 107600. https://doi.org/10.1016/j.chb.2022.107600
19.
TenhundfeldN. L.de VisserE. J.HaringK. S.RiesA. J.FinomoreV. S.TossellC. C. (2019). Calibrating trust in automation through familiarity with the autoparking feature of a Tesla Model X. Journal of Cognitive Engineering and Decision Making, 13(4), 279–294. https://doi.org/10.1177/1555343419869083
20.
TenhundfeldN. L.BarrH. M.O’HearE. H.WegerK. (2022). Is My Siri the Same as Your Siri? An Exploration of Users’ Mental Model of Virtual Personal Assistants, Implications for Trust. IEEE Transactions on Human Machine Systems, 52(3), 512–521.
21.
TenhundfeldN. L.de VisserE. J.RiesA. J.FinomoreV. S.TossellC. C. (2020). Trust and Distrust of Automated Parking in a Tesla Model X. Human Factors, 62(2), 194–210. https://doi.org/10.1177/0018720819865412
22.
ZhangY.SunS.GalleyM.ChenY.-C.BrockettC.GaoX.GaoJ.LiuJ.DolanB. (2020). DialoGPT: Large-Scale Generative Pre- training for Conversational Response Generation (arXiv:1911.00536). arXiv. http://arxiv.org/abs/1911.00536
