Exploring AI and Dialogic Education Outcomes From a Learning Sciences Perspective

Background To The Study

Emerging concerns about AI’s fictional characteristics associated with learning unexpected information from data arrays originated from the West and little algorithmic inputs from the Global South (Kaivo-oja, Roth, & Westerlund, 2017), false logics and abuses non-transparency and data control by users, evaluation bias and heterogeneous definitions have been noted (Westerlund, 2019; Sanderson, 2023) are some contributing factors to low generative AI integration in learning. Tensions also exist between humans and AI decision making, teacher and learner control issues in classrooms, and intellectual copyrights (U.S. Department of Education, 2023). While the optimism behind technology in education is certainly forward-looking for students’ achievements and future of work, clear indication of how such AI tools could impact their deep learning outcomes are fuzzy. Certainly, human cognition is required to evaluate AI’s generative information and its impact on deep pedagogy as faculty members’ professional knowledge about student learning outcomes are important for critical thinking, collaborative and digital algorithm skills, technical communication, conceptual thinking, and global citizenship skills ought to be examined from Learning Scientists’ perspective in the Digital Age. It would seem that empirical discourse about generative AI, dialogic pedagogy, and algorithmic skills amongst higher education faculty remains relatively unexamined form Learning Sciences domain in the Global South.

Learning Sciences (LS) is an interdisciplinary field that examines learning across a range of environments such as formal and informal settings, and social groups (Giannakos & Cukurova, 2022); it strives to gain a deeper understanding of the cognitive and social processes that lead to effective learning outcomes (Sawyer, 2006). The sciences of learning encompass diverse fields including cognitive science, educational psychology, computer science, anthropology, sociology, information sciences, neurosciences, education, design studies, and instructional design, amongst others (Lee, 2023). As a nascent discipline, the field of Learning Sciences (LS) is still in the process of defining itself (Dede et al., 2018) and guided by theories of learning such as constructivism, social constructivism, socio-cognitive, and socio-cultural (Sawyer, 2022). While cognitive science, emerging technologies, and interactions in learning ecosystem have potentially impacted knowledge acquisition across life spans (Evans et al., 2016), LS answers critical questions around inclusion and access, inquiry-based pedagogy, and learning environments. Therefore, the role of LS in conceptualisation of instructional outcomes in the wake of educational reforms such as AI’s impacts has been advanced (Sawyer, 2006).

As AI programming expands, deep learning outcomes should be championed by professional teachers as most universities students are leapfrogging their faculty members in its adoption in lieu of traditional classroom experiences that rely on regurgitation of information. As digital natives operating in hyperconnectivity world, today’s students are increasing using digital devices to access information but they will require pedagogical coaching that aligns with dialogic education goals from instructors (Buabeng et al., 2020). Involvement of LS in digitally mediated learning environments would also mitigate the impact of global health on education as over 229,756 learners dropped out of school (Statistica, 2023), with 93,640 failing in STEM subjects at the senior high school level in Ghana (Bonney, 2023) during COVID-19 pandemic alone. Within the context of Ghana’s Education Sector Plan 2018-2030 which seeks improved equitable access and participation in quality, deepen inquiry-based learning outcomes, and achieve Sustainable Development Goal 4, integration of LS principles into AI deployment has the potential to transform higher education in the Global South. Importantly, students are already using AI but they will require pedagogical guidance to connect theory to practice while solving real-world scenarios.