Abstract
Artificial intelligence (AI) technologies provide useful assistance for online learning and teaching, such as individualized instruction for students, automation of faculty responsibilities, and adaptive learning assessment. Although AI has promising applications, it is yet unclear how these technologies will affect the norms and expectations of the student-teacher dynamic. Interaction between teachers and students is a key factor in the success of online courses (including communication, assistance, and presence). Understanding how students and teachers see the influence of AI systems on their interactions is crucial for locating any gaps, hurdles, or constraints preventing AI systems from reaching their full potential and jeopardizing the sanctity of these interactions. For this foresightful investigation, we conducted a storyboard study of the opinions of 9 students and 8 professors about various use cases of potential AI systems in online learning. Participants predict the widespread use of AI systems in online education to promote individualized learner-instructor engagement, despite the fact that doing so carries the potential for ethical violations. Even though AI systems have been lauded for improving both the quantity and quality of communication, for providing large-scale settings with just-in-time, customized assistance, and for fostering a greater sense of connection, worries have been raised about accountability, agency, and surveillance. These results have important implications for the development of AI systems, particularly with regard to facilitating their explicability, interaction with humans, and comprehensiveness of data collection and display.
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References
Alam, A.: Challenges and possibilities in teaching and learning of calculus: a case study of India. J. Educ. Gifted Young Sci. 8(1), 407–433 (2020)
Franzoni, V., Milani, A., Mengoni, P., Piccinato, F.: Artificial intelligence visual metaphors in e-learning interfaces for learning analytics. Appl. Sci. 10(20), 7195 (2020)
Alam, A.: Pedagogy of calculus in India: an empirical investigation. Periódico Tchê QuÃmica 17(34), 164–180 (2020)
Raj, N.S., Renumol, V.G.: Early prediction of student engagement in virtual learning environments using machine learning techniques. E-Learn. Digit. Media 19(6), 537–554 (2022). https://doi.org/10.1177/20427530221108027
Alam, A.: Possibilities and challenges of compounding artificial intelligence in India’s educational landscape. Int. J. Adv. Sci. Technol. 29(5), 5077–5094 (2020)
Bagunaid, W., Chilamkurti, N., Veeraraghavan, P.: AISAR: artificial intelligence-based student assessment and recommendation system for E-learning in big data. Sustainability 14(17), 10551 (2022)
Alam, A.: Test of knowledge of elementary vectors concepts (TKEVC) among first-semester bachelor of engineering and technology students. Periódico Tchê QuÃmica 17(35), 477–494 (2020)
Alam, A.: Should robots replace teachers? Mobilisation of AI and learning analytics in education. In: 2021 International Conference on Advances in Computing, Communication, and Control (ICAC3), pp. 1–12. IEEE (2021)
Ayouni, S., Hajjej, F., Maddeh, M., Al-Otaibi, S.: A new ML-based approach to enhance student engagement in online environment. PLoS ONE 16(11), e0258788 (2021)
Alam, A.: Possibilities and apprehensions in the landscape of artificial intelligence in education. In: 2021 International Conference on Computational Intelligence and Computing Applications (ICCICA), pp. 1–8. IEEE (2021)
Alam, A.: Educational robotics and computer programming in early childhood education: a conceptual framework for assessing elementary school students’ computational thinking for designing powerful educational scenarios. In: 2022 International Conference on Smart Technologies and Systems for Next Generation Computing (ICSTSN), pp. 1–7. IEEE (2022)
Yin, W.: An artificial intelligent virtual reality interactive model for distance education. J. Math. 2022, 7099963 (2022)
Alam, A.: A digital game based learning approach for effective curriculum transaction for teaching-learning of artificial intelligence and machine learning. In: 2022 International Conference on Sustainable Computing and Data Communication Systems (ICSCDS), pp. 69–74. IEEE (2022)
Alam, A.: Investigating sustainable education and positive psychology interventions in schools towards achievement of sustainable happiness and wellbeing for 21st century pedagogy and curriculum. ECS Trans. 107(1), 19481 (2022)
Schiff, D.: Out of the laboratory and into the classroom: the future of artificial intelligence in education. AI Soc. 36(1), 331–348 (2020). https://doi.org/10.1007/s00146-020-01033-8
Alam, A.: Social robots in education for long-term human-robot interaction: socially supportive behaviour of robotic tutor for creating robo-tangible learning environment in a guided discovery learning interaction. ECS Trans. 107(1), 12389 (2022)
Greenhow, C., Graham, C.R., Koehler, M.J.: Foundations of online learning: challenges and opportunities. Educ. Psychol. 57(3), 131–147 (2022)
Alam, A.: Positive psychology goes to school: conceptualizing students’ happiness in 21st century schools while ‘minding the mind!’ Are we there yet? Evidence-backed, school-based positive psychology interventions. ECS Trans. 107(1), 11199 (2022)
Yildirim, Y., Celepcikay, A.: Artificial intelligence and machine learning applications in education. Eurasian J. High. Educ. 2(4), 1–11 (2021)
Alam, A.: Mapping a sustainable future through conceptualization of transformative learning framework, education for sustainable development, critical reflection, and responsible citizenship: an exploration of pedagogies for twenty-first century learning. ECS Trans. 107(1), 9827 (2022)
Chung, Y., Kim, J.R.: Is artificial intelligence (AI) lecturer acceptable for adult learners in distance education?: An exploratory study on a cyber university, South Korea (2020)
Alam, A.: Employing adaptive learning and intelligent tutoring robots for virtual classrooms and smart campuses: reforming education in the age of artificial intelligence. In: Shaw, R.N., Das, S., Piuri, V., Bianchini, M. (eds.) Advanced Computing and Intelligent Technologies, vol. 914, pp. 395–406. Springer, Singapore (2022). https://doi.org/10.1007/978-981-19-2980-9_32
Greener, S.: The tensions of student engagement with technology. Interact. Learn. Environ. 30(3), 397–399 (2022)
Alam, A.: Cloud-based E-learning: scaffolding the environment for adaptive E-learning ecosystem based on cloud computing infrastructure. In: Satapathy, S.C., Lin, J.C.W., Wee, L.K., Bhateja, V., Rajesh, T.M. (eds.) Computer Communication, Networking and IoT, vol. 459, pp. 1–9. Springer, Singapore (2023). https://doi.org/10.1007/978-981-19-1976-3_1
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Alam, A., Mohanty, A. (2023). Facial Analytics or Virtual Avatars: Competencies and Design Considerations for Student-Teacher Interaction in AI-Powered Online Education for Effective Classroom Engagement. In: Tomar, R.S., et al. Communication, Networks and Computing. CNC 2022. Communications in Computer and Information Science, vol 1894. Springer, Cham. https://doi.org/10.1007/978-3-031-43145-6_21
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