ChatGPT and Improvement in Productivity: An analytical Study
DOI:
https://doi.org/10.61506/01.00512Keywords:
GPT, Open AI, Efficiency, Productivity, Creativity, EducatorsAbstract
This paper attempts to investigate how ChatGPT, an artificial intelligence-based language model made by OpenAI, can be leveraged to enhance productivity in the field of education attractive to both educators and learners. There is an increasing provision for customized and administratively effective approaches to education and ChatGPT avails in a number of ways; automating tiresome and mundane activities, content development and even acting as a tutor. This paper seeks to assess the impact of ChatGPT on improving productivity levels, decreasing workload and enhancing creativity in educational settings. A mixed-method perspective was employed. First, a quantitative survey was done with 100 teachers of public and private schools where biographic as well as data on AI in lesson planning, grading and interaction with students was collected. Second, 20 interviews were held with both educators and students to capture their experiences with AI tools focusing on the advantages and disadvantages of adoption. Classroom cases were manipulated in order to find out the effectiveness of Chatgpt on the productivity in the course of its use. It has been found from the results that Chatgpt saves the time that could be taken per educator to perform certain general tasks, especially essay writing and lesson plan preparation, as more time is now available for teaching. There are benefits for the students since the system can offer timely responses and make teaching resources for learners on a case basis. Such worries imply that one is likely to suffer from too much reliance on AI technologies, or the written content will not be of high quality, hence calling for a cautious approach to implementation. The study ends by indicating that irrespective of the fact that ChatGPT heightens the educational outputs in the school, it is clear that, it should be an additional tool and should not replace conventional teaching strategies.
References
Baker, R. S., Wang, Y., Paquette, L., & Francis, J. (2021). Scaling up automated writing evaluation for middle grades in classrooms: The next steps. Journal of Educational Computing Research, 59(5), 1014-1043.
Bender, E., Gebru, T., McMillan-Major, A., &Shmitchell, S. (2021). On the dangers of stochastic parrots: Can language models be too big? Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency (pp. 610-623). DOI: https://doi.org/10.1145/3442188.3445922
Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., &Amodei, D. (2020). Language models are few-shot learners. Advances in Neural Information Processing Systems, 33, 1877-1901.
Gautam, C. (2023). AI in education: Opportunities and challenges in content creation and lesson planning. Journal of Educational Technology, 40(2), 98-115.
Holmes, W., Bialik, M., &Fadel, C. (2019). Artificial intelligence in education: Promises and implications for teaching and learning. Center for Curriculum Redesign.
Koedinger, K. R., & Corbett, A. T. (2006). Cognitive tutors: Technology bringing learning science to the classroom. In R. K. Sawyer (Ed.), The Cambridge handbook of the learning sciences (pp. 61-78). Cambridge University Press. DOI: https://doi.org/10.1017/CBO9780511816833.006
Luckin, R. (2017). Machine learning and human intelligence: The future of education for the 21st century. UCL Institute of Education Press.
Mishra, P., & Koehler, M. J. (2006). Technological pedagogical content knowledge: A framework for teacher knowledge. Teachers College Record, 108(6), 1017-1054. DOI: https://doi.org/10.1177/016146810610800610
Noble, S. U. (2018). Algorithms of oppression: How search engines reinforce racism. NYU Press. DOI: https://doi.org/10.2307/j.ctt1pwt9w5
Puentedura, R. R. (2006). Transformation, technology, and education. Selwyn, N. (2019). Should robots replace teachers? Education and Information Technologies, 24(4), 3039-3056.
Williamson, B., &Eynon, R. (2020). Surveillance, big data and the future of education. Oxford Review of Education, 46(1), 1-17.
Zawacki-Richter, O., Marín, V. I., Bond, M., &Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education – Where are the educators? International Journal of Educational Technology in Higher Education, 16(1), 39. DOI: https://doi.org/10.1186/s41239-019-0171-0