First Team Blog-Ano, Asha, Catherine, Radhika, and Tracy,

We were asked to choose a learning technology and an event in this course. After discussing interesting topics, we decided on artificial intelligence (AI) with personalized learning. Upon researching online courses, we initially chose a course from the Udemy platform; as we began the course, we found that it needed to be more informative. We then chose a class on Coursera called Innovative Teaching Through ChatGPT; this class gave more examples of how to personalize education using ChatGPT. However, this class was only the beginning of our research. 

We each chose two articles or books to read to understand AI in personalized learning better. We found some similarities; for instance, both Selwyn (2019) and Magomadov (2020) discussed the importance of student-teacher relationships and how technology should not replace teachers but rather enhance their teaching and lessons. Each article discussed different benefits and challenges of using AI in personalized learning. 

One promising aspect Van der Vorst & Jelicic (2019) discussed was how AI can revolutionize learning experiences by tailoring them to individual students' unique needs and preferences. They also explored the potential and challenges of implementing AI-driven personalized learning approaches. Akgun and Greenhow (2021) further emphasized the benefits, including using AI with automated assessment and predictive analytics. They believe that leveraging AI in these areas can significantly alleviate the administrative burden on teachers. Murtaza et al. (2022) also examined the change from conventional e-learning to a more personalized learning approach emphasizing tailored content and individual assessment. The articles underscored these advantages and acknowledged the need to address the associated challenges. 

We found various concerns in our research that had to do with ethics, privacy, and biases. For instance, Regan & Jesse (2019) pointed out that using AI raises questions about gathering data and how they are tracking and grouping students, which may be discriminatory. Further, the authors discussed that the interests of one group may overshadow those of different groups, specifically looking at vendors and educational technology programs. Yang et al. (2021) also mentioned the need for more governance as the lack of it, in combination with algorithm bias, could lead to further inequality. Al-Badi et al. (2022) looked into the perceptions that students and instructors had on using AI for personalized learning. The authors discovered mixed feelings as some found using these tools helpful to their learning. On the other hand, some were weary of using the technology as they expressed concern over privacy, trust, and the limited capabilities of using AI for personalized learning. These concerns and issues are all valid, and we will continue to address them throughout our research, looking at them in more detail. 

Building on our initial research, we are committed to further exploring the use of AI in personalized learning through a critical lens, considering ethical, political, and cultural perspectives. As we delve deeper, we invite you to join us in pondering the following questions that we believe are crucial to our understanding of this complex topic:  

• How can AI-powered learning systems effectively engage individuals to obtain more precise data, facilitating the development of diverse learner personas that enable educators and researchers to gain deeper insights into individual attitudes and behaviours?

• How can we ensure that the information given to students is credible and accurate when using AI for personalized learning? 

• What is the role of instructors and learning designers in improving the quality of personalized learning recommendations? Who is responsible for governing how these vendors operate? 

Asha, Ano, Catherine, Radhika, and Tracy,

References 

Akgun, S., & Greenhow, C. (2021). Artificial intelligence in education: Addressing ethical challenges in K-12 settings. AI and Ethics, 2(3). https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8455229/

Al-Badi, A., Khan, A., & Eid-Alotaibi. (2022). Perceptions of learners and instructors towards artificial intelligence in personalized learning. Procedia Computer Science, 201, 445–451. https://doi.org/10.1016/j.procs.2022.03.058

Magomadov, V. S. (2020). The application of artificial intelligence and big data analytics in personalized learning. Journal of Physics: Conference Series, 1691(1), 012169. https://doi.org/10.1088/1742-6596/1691/1/012169

Murtaza, M., Ahmed, Y., Shamsi, J. A., Sherwani, F., & Usman, M. (2022). AI-Based personalized e-learning systems: Issues, challenges, and solutions. IEEE Access, 10(1), 81323–81342. https://doi.org/10.1109/ACCESS.2022.3193938

Regan, P. M., & Jesse, J. (2018). Ethical challenges of edtech, big data and personalized learning: Twenty-first century student sorting and tracking. Ethics and Information Technology, 21(3), 167–179. https://doi.org/10.1007/s10676-018-9492-2

Selwyn, N. (2019). Should robots replace teachers? AI and the future of education. Polity Press.

van der Vorst, T., & Jelicic, N. (2019). Artificial intelligence in education: Can AI bring the full potential of personalized learning to education? Www.econstor.eu; Calgary: International Telecommunications Society (ITS). https://www.econstor.eu/handle/10419/205222

Yang, S. J. H., Ogata, H., Matsui, T., & Chen, N.-S. (2021). Human-centered artificial intelligence in education: Seeing the invisible through the visible. Computers and Education: Artificial Intelligence, 2, 100008. https://doi.org/10.1016/j.caeai.2021.100008