Abstract
Background. The presence of ChatGPT which is used continuously will make students lose their motivation to study. If students continue to use ChatGPT, this will become a problem for the students themselves. For not doing the job according to his ability. So students will not get value with what they get.
Purpose. this is done to find out the threat to the world of education: research on the use of ChatGPT.
Method. using quantitative methods, data obtained through interviews and distributing questionnaires online using the Google form.
Results. resultThis explains that the danger of continuous use of ChatGPT will be a bad influence on students. Where the enthusiasm for student learning decreases, when they get assignments from their lecturers. These students will look for answers in ChatGPT and they will not use existing sources such as books, articles and journals.
Conclusion. limitationsThis research is only conducted at a university in an Arab country
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References
Abbas, J., Aman, J., Nurunnabi, M., & Bano, S. (2019). The Impact of Social Media on Learning Behavior for Sustainable Education: Evidence of Students from Selected Universities in Pakistan. Sustainability, 11(6), 1683.https://doi.org/10.3390/su11061683
Androutsopoulou, A., Karacapilidis, N., Loukis, E., & Charalabidis, Y. (2019). Transforming the communication between citizens and government through AI-guided chatbots. Government Information Quarterly, 36(2), 358–367.https://doi.org/10.1016/j.giq.2018.10.001
Araujo, T. (2018). Living up to the chatbot hype: The influence of anthropomorphic design cues and communicative agency framing on conversational agents and company perceptions. Computers in Human Behavior, 85, 183–189.https://doi.org/10.1016/j.chb.2018.03.051
Ashfaq, M., Yun, J., Yu, S., & Loureiro, SMC (2020). I, Chatbot: Modeling the determinants of users' satisfaction and continuance intention of AI-powered service agents. Telematics and Informatics, 54, 101473.https://doi.org/10.1016/j.tele.2020.101473
Burley, SK, Berman, HM, Bhikadiya, C., Bi, C., Chen, L., Di Costanzo, L., Christie, C., Dalenberg, K., Duarte, JM, Dutta, S., Feng, Z., Ghosh, S., Goodsell, DS, Green, RK, Guranovi?, V., Guzenko, D., Hudson, BP, Kalro, T., Liang, Y., … Zardecki, C. (2019). RCSB Protein Data Bank: Biological macromolecular structures enabling research and education in fundamental biology, biomedicine, biotechnology and energy. Nucleic Acids Research, 47(D1), D464–D474.https://doi.org/10.1093/nar/gky1004
Chattaraman, V., Kwon, W.-S., Gilbert, JE, & Ross, K. (2019). Should AI-Based, conversational digital assistants employ social- or task-oriented interaction style? A task-competency and reciprocity perspective for older adults. Computers in Human Behavior, 90, 315–330.https://doi.org/10.1016/j.chb.2018.08.048
Ciechanowski, L., Przegalinska, A., Magnuski, M., & Gloor, P. (2019). In the shades of the uncanny valley: An experimental study of human–chatbot interaction. Future Generation Computer Systems, 92, 539–548.https://doi.org/10.1016/j.future.2018.01.055
Duke, NK, Ward, AE, & Pearson, PD (2021). The Science of Reading Comprehension Instruction. The Reading Teacher, 74(6), 663–672.https://doi.org/10.1002/trtr.1993
Dwivedi, YK, Hughes, L., Ismagilova, E., Aarts, G., Coombs, C., Crick, T., Duan, Y., Dwivedi, R., Edwards, J., Eirug, A., Galanos , V., Ilavarasan, PV, Janssen, M., Jones, P., Kar, AK, Kizgin, H., Kronemann, B., Lal, B., Lucini, B., … Williams, MD (2021). Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. International Journal of Information Management, 57, 101994.https://doi.org/10.1016/j.ijinfomgt.2019.08.002
Fryer, LK, Nakao, K., & Thompson, A. (2019). Chatbot learning partners: Connecting learning experiences, interests and competence. Computers in Human Behavior, 93, 279–289.https://doi.org/10.1016/j.chb.2018.12.023
Gasnikov, AV, & Kovalev, DA (2018). A hypothesis about the rate of global convergence for optimal methods (Newtons type) in smooth convex optimization. Computer Research and Modeling, 10(3), 305–314.https://doi.org/10.20537/2076-7633-2018-10-3-305-314
Go, E., & Sundar, SS (2019). Humanizing chatbots: The effects of visual, identity and conversational cues on human perceptions. Computers in Human Behavior, 97, 304–316.https://doi.org/10.1016/j.chb.2019.01.020
Greczynski, G., & Hultman, L. (2020). Compromising Science by Ignorant Instrument Calibration—Need to Revisit Half a Century of Published XPS Data. Angewandte Chemie International Edition, 59(13), 5002–5006.https://doi.org/10.1002/anie.201916000
Hao, Y., Hao, S., Andersen-Nissen, E., Mauck, WM, Zheng, S., Butler, A., Lee, MJ, Wilk, AJ, Darby, C., Zager, M., Hoffman , P., Stoeckius, M., Papalexi, E., Mimitou, EP, Jain, J., Srivastava, A., Stuart, T., Fleming, LM, Yeung, B., … Satija, R. (2021) . Integrated analysis of multimodal single-cell data. Cell, 184(13), 3573-3587.e29.https://doi.org/10.1016/j.cell.2021.04.048
Hoyer, WD, Kroschke, M., Schmitt, B., Kraume, K., & Shankar, V. (2020). Transforming the Customer Experience Through New Technologies. Journal of Interactive Marketing, 51, 57–71.https://doi.org/10.1016/j.intmar.2020.04.001
Hwangbo, J., Lee, J., Dosovitskiy, A., Bellicoso, D., Tsounis, V., Koltun, V., & Hutter, M. (2019). Learning agile and dynamic motor skills for legged robots. Science Robotics, 4(26), eaau5872.https://doi.org/10.1126/scirobotics.aau5872
James, SL, Abate, D., Abate, KH, Abay, SM, Abbafati, C., Abbasi, N., Abbastabar, H., Abd-Allah, F., Abdela, J., Abdelalim, A., Abdollahpour , I., Abdulkader, RS, Abebe, Z., Abera, SF, Abil, OZ, Abraha, HN, Abu-Raddad, LJ, Abu-Rmeileh, NME, Accrombessi, MMK, … Murray, CJL (2018). Global, regional, and national incidence, prevalence, and years lived with disability for 354 diseases and injuries for 195 countries and territories, 1990–2017: A systematic analysis for the Global Burden of Disease Study 2017. The Lancet, 392(10159), 1789–1858.https://doi.org/10.1016/S0140-6736(18)32279-7
Joo, YJ, So, H.-J., & Kim, NH (2018). Examination of relationships among students' self-determination, technology acceptance, satisfaction, and continuance intention to use K-MOOCs. Computers & Education, 122, 260–272.https://doi.org/10.1016/j.compedu.2018.01.003
Keller, J., Mendes, K., & Ringrose, J. (2018). Speaking 'unspeakable things': Documenting digital feminist responses to rape culture. Journal of Gender Studies, 27(1), 22–36.https://doi.org/10.1080/09589236.2016.1211511
Khan, T., Johnston, K., & Ophoff, J. (2019). The Impact of an Augmented Reality Application on Learning Motivation of Students. Advances in Human-Computer Interaction, 2019, 1–14.https://doi.org/10.1155/2019/7208494
Kim, S., Chen, J., Cheng, T., Gindulyte, A., He, J., He, S., Li, Q., Shoemaker, BA, Thiessen, PA, Yu, B., Zaslavsky, L., Zhang, J., & Bolton, E.E. (2021). PubChem in 2021: New data content and improved web interfaces. Nucleic Acids Research, 49(D1), D1388–D1395.https://doi.org/10.1093/nar/gkaa971
Korhonen, J., Honkasalo, A., & Seppälä, J. (2018). Circular Economy: The Concept and its Limitations. Ecological Economics, 143, 37–46.https://doi.org/10.1016/j.ecolecon.2017.06.041
Korstjens, I., & Moser, A. (2018). Series: Practical guidance to qualitative research. Part 4: Trustworthiness and publishing. European Journal of General Practice, 24(1), 120–124.https://doi.org/10.1080/13814788.2017.1375092
Lazer, DMJ, Baum, MA, Benkler, Y., Berinsky, AJ, Greenhill, KM, Menczer, F., Metzger, MJ, Nyhan, B., Pennycook, G., Rothschild, D., Schudson, M., Sloman, SA, Sunstein, CR, Thorson, EA, Watts, DJ, & Zittrain, JL (2018). The science of fake news. Science, 359(6380), 1094–1096.https://doi.org/10.1126/science.aao2998
Levine, S., Pastor, P., Krizhevsky, A., Ibarz, J., & Quillen, D. (2018). Learning hand-eye coordination for robotic grasping with deep learning and large-scale data collection. The International Journal of Robotics Research, 37(4–5), 421–436.https://doi.org/10.1177/0278364917710318
Lewis, SC, Guzman, AL, & Schmidt, TR (2019). Automation, Journalism, and Human–Machine Communication: Rethinking Roles and Relationships of Humans and Machines in News. Digital Journalism, 7(4), 409–427.https://doi.org/10.1080/21670811.2019.1577147
Low, ES, Ong, P., & Cheah, KC (2019). Solving the optimal path planning of a mobile robot using improved Q-learning. Robotics and Autonomous Systems, 115, 143–161.https://doi.org/10.1016/j.robot.2019.02.013
Mouni, L., Belkhiri, L., Bollinger, J.-C., Bouzaza, A., Assadi, A., Tirri, A., Dahmoune, F., Madani, K., & Remini, H. (2018 ). Removal of Methylene Blue from aqueous solutions by adsorption on Kaolin: Kinetic and equilibrium studies. Applied Clay Science, 153, 38–45.https://doi.org/10.1016/j.clay.2017.11.034
Murphy, MPA (2020). COVID-19 and emergency eLearning: Consequences of the securitization of higher education for post-pandemic pedagogy. Contemporary Security Policy, 41(3), 492–505.https://doi.org/10.1080/13523260.2020.1761749
Neubauer, S., Hublin, J.-J., & Gunz, P. (2018). The evolution of modern human brain shape. Science Advances, 4(1), eaao5961.https://doi.org/10.1126/sciadv.aao5961
Ortiz, C., Ortiz-Peregrina, S., Castro, JJ, Casares-López, M., & Salas, C. (2018). Driver distraction by smartphone use (WhatsApp) in different age groups. Accident Analysis & Prevention, 117, 239–249.https://doi.org/10.1016/j.aap.2018.04.018
Patricia Aguilera-Hermida, A. (2020). College students' use and acceptance of emergency online learning due to COVID-19. International Journal of Educational Research Open, 1, 100011.https://doi.org/10.1016/j.ijedro.2020.100011
Pennycook, G., & Rand, DG (2019). Lazy, not biased: Susceptibility to partisan fake news is better explained by lack of reasoning than by motivated reasoning. Cognition, 188, 39–50.https://doi.org/10.1016/j.cognition.2018.06.011
Perez-Riverol, Y., Csordas, A., Bai, J., Bernal-Llinares, M., Hewapathirana, S., Kundu, DJ, Inuganti, A., Griss, J., Mayer, G., Eisenacher, M., Pérez, E., Uszkoreit, J., Pfeuffer, J., Sachsenberg, T., Y?lmaz, ?., Tiwary, S., Cox, J., Audain, E., Walzer, M., … Vizcaíno , JA (2019). The PRIDE database and related tools and resources in 2019: Improving support for quantification data. Nucleic Acids Research, 47(D1), D442–D450.https://doi.org/10.1093/nar/gky1106
Phillippi, J., & Lauderdale, J. (2018). A Guide to Field Notes for Qualitative Research: Context and Conversation. Qualitative Health Research, 28(3), 381–388.https://doi.org/10.1177/1049732317697102
Rodriques, SG, Stickels, RR, Goeva, A., Martin, CA, Murray, E., Vanderburg, CR, Welch, J., Chen, LM, Chen, F., & Macosko, EZ (2019). Slide-seq: A scalable technology for measuring genome-wide expression at high spatial resolution. Science, 363(6434), 1463–1467.https://doi.org/10.1126/science.aaw1219
Roozenbeek, J., Schneider, CR, Dryhurst, S., Kerr, J., Freeman, ALJ, Recchia, G., van der Bles, AM, & van der Linden, S. (2020). Susceptibility to misinformation about COVID-19 around the world. Royal Society Open Science, 7(10), 201199.https://doi.org/10.1098/rsos.201199
Ryan, RM, & Deci, EL (2020). Intrinsic and extrinsic motivation from a self-determination theory perspective: Definitions, theory, practices, and future directions. Contemporary Educational Psychology, 61, 101860.https://doi.org/10.1016/j.cedpsych.2020.101860
Saberi, S., Kouhizadeh, M., Sarkis, J., & Shen, L. (2019). Blockchain technology and its relationships to sustainable supply chain management. International Journal of Production Research, 57(7), 2117–2135.https://doi.org/10.1080/00207543.2018.1533261
Sanders, JJ, Curtis, JR, & Tulsky, JA (2018). Achieving Goal-Concordant Care: A Conceptual Model and Approach to Measuring Serious Illness Communication and Its Impact. Journal of Palliative Medicine, 21(S2), S-17-S-27.https://doi.org/10.1089/jpm.2017.0459
Szklarczyk, D., Gable, AL, Lyon, D., Junge, A., Wyder, S., Huerta-Cepas, J., Simonovic, M., Doncheva, NT, Morris, JH, Bork, P., Jensen , LJ, & Mering, C. von. (2019). STRING v11: Protein–protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Research, 47(D1), D607–D613.https://doi.org/10.1093/nar/gky1131
Theobald, EJ, Hill, MJ, Tran, E., Agrawal, S., Arroyo, EN, Behling, S., Chambwe, N., Cintrón, DL, Cooper, JD, Dunster, G., Grummer, JA, Hennessey , K., Hsiao, J., Iranon, N., Jones, L., Jordt, H., Keller, M., Lacey, ME, Littlefield, CE, … Freeman, S. (2020). Active learning narrows achievement gaps for underrepresented students in undergraduate science, technology, engineering, and math. Proceedings of the National Academy of Sciences, 117(12), 6476–6483.https://doi.org/10.1073/pnas.1916903117
Théry, C., Witwer, KW, Aikawa, E., Alcaraz, MJ, Anderson, JD, Andriantsitohaina, R., Antoniou, A., Arab, T., Archer, F., Atkin-Smith, GK, Ayre, DC, Bach, J. -M., Bachurski, D., Baharvand, H., Balaj, L., Baldacchino, S., Bauer, NN, Baxter, AA, Bebawy, M., … Zuba-Surma, EK ( 2018). Minimal information for studies of extracellular vesicles 2018 (MISEV2018): A position statement of the International Society for Extracellular Vesicles and an update of the MISEV2014 guidelines. Journal of Extracellular Vesicles, 7(1), 1535750.https://doi.org/10.1080/20013078.2018.1535750
Voorveld, HAM, van Noort, G., Muntinga, DG, & Bronner, F. (2018). Engagement with Social Media and Social Media Advertising: The Differentiating Role of Platform Type. Journal of Advertising, 47(1), 38–54.https://doi.org/10.1080/00913367.2017.1405754
Wang, Y., Sun, Y., Liu, Z., Sarma, SE, Bronstein, MM, & Solomon, JM (2019). CNN Dynamic Graph for Learning on Point Clouds. ACM Transactions on Graphics, 38(5), 1–12.https://doi.org/10.1145/3326362
Widyartha, B., Setiyorini, Y., Abdul, F., Subakti, TJ, & Pintowantoro, S. (2020). Effective beneficiation of low content nickel ferrous laterite using a fluxing agent through selective reduction of Na 2 SO 4. Materialwissenschaft Und Werkstofftechnik, 51(6), 750–757.https://doi.org/10.1002/mawe.202000007
Wu, Q., Zeng, Y., & Zhang, R. (2018). Joint Trajectory and Communication Design for Multi-UAV Enabled Wireless Networks. IEEE Transactions on Wireless Communications, 17(3), 2109–2121.https://doi.org/10.1109/TWC.2017.2789293
Zech, JR, Badgeley, MA, Liu, M., Costa, AB, Titano, JJ, & Oermann, EK (2018). Variable generalization performance of a deep learning model to detect pneumonia in chest radiographs: A cross-sectional study. PLOS Medicine, 15(11), e1002683.https://doi.org/10.1371/journal.pmed.1002683
Zhao, R., Yan, R., Chen, Z., Mao, K., Wang, P., & Gao, RX (2019). Deep learning and its applications to machine health monitoring. Mechanical Systems and Signal Processing, 115, 213–237.https://doi.org/10.1016/j.ymssp.2018.05.050
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