Some Reflections on Learning Design and AI

Large Language Models (LLMs) present practical opportunities for educators to embrace evidence-based instructional design practices with ease while designing diverse learning experiences. These tools can generate content, provide real-time feedback, and automate routine tasks, potentially freeing up valuable time for teachers to focus on higher-order instructional design—the sequencing of instruction or the differentiation of the pace and path of student learning, for example.

"Defining the key learning outcomes, creating appropriate learning materials, and orchestrating the learning approach will continue to be at the epicentre of LD" (Giannakos et al., 2024). This is undoubtedly true. A learning environment is created by the teacher, which is to say, at its best, it's designed. The authors go on to argue that Generative AI tools present practical opportunities for educators but that it is essential to weigh their limitations and ethical concerns carefully. The prudent exploration of educational technology with an eye to your instructional principles is certainly sound advice to a teacher, if a bit laborious to point out.

Beyond automation, AI has the potential to bridge the gap between research and practice by making evidence-based teaching methods more accessible to everyday practitioners. Instead of spending hours manually adapting resources or searching for the latest pedagogical research, educators can leverage AI to surface best practices and generate instructional content that aligns with proven strategies. This capacity not only lowers the cost of exploration but also democratizes access to quality teaching resources, creating foundations for a more dynamic, iterative, and practice-oriented Scholarship of Teaching and Learning.

Balancing Opportunities with Caution

The implications for student-facing AI carry more weight, given the magnified nature of its effects—the avenues for misuse are more apparent, and the concerns around hallucinations more salient (as Erasmus put it, "For, remembering how difficult it is to eradicate early impressions, we should aim from the first at learning what need never be unlearnt, and that only (Woodward, 1904)).

The use of AI in education also raises ethical questions around data privacy, student autonomy, and the potential for misuse. AI tools, if not carefully managed, could reinforce biases, compromise students’ privacy, or be used in ways that undermine the learning process. The implications of these technologies extend far beyond simple technical challenges—they strike at the heart of educational ethics and the teacher-student relationship.

Moreover, the rapid deployment of AI technologies in classrooms necessitates robust policy development to guide their ethical use. Schools and educational institutions must work to establish clear guidelines that address not only the practical application of AI but also its broader impact on pedagogy and student well-being. Without these safeguards, the integration of AI risks outpacing our ability to manage its consequences effectively.

Moving Toward a New Era of Evidence-Based Practice

To fully realize the potential of GenAI in supporting effective teaching and learning, further research is essential. Studies should focus on the long-term impact of AI on learning outcomes, exploring how these tools can be used to enhance rather than replace the human elements of education. As researchers, policymakers, and educators collaborate, we can begin to develop frameworks that ensure AI technologies are used responsibly and effectively, aligning with best practices in pedagogy.

To achieve this goal, it is essential to engage in ongoing dialogue among all stakeholders in the education sector. Teachers, for instance, can provide valuable insights into how AI tools can be integrated into their classrooms to support diverse learning contexts. By sharing their experiences and challenges, educators can help shape the development of AI applications that truly meet the needs of students.

Moreover, it is crucial to consider the ethical implications of AI in education. As we embrace these technologies, we must ensure that they promote equity and accessibility for all learners. This means addressing potential biases in AI algorithms and ensuring that all students, regardless of their background, have equal access to the benefits of these innovations.

Professional development for educators will also play a key role in this transition. Training programs should equip teachers with the skills to effectively use AI tools, reinforcing a culture of continuous learning and adaptation. In addition, research should explore the emotional and social dimensions of learning with AI. Understanding how students interact with these technologies can provide insights into their engagement and motivation. This knowledge can inform the design of AI systems that not only support academic achievement but also nurture students' social and emotional well-being.

Ultimately, the successful integration of AI in education requires a collaborative approach that prioritizes the needs of learners. By focusing on the human aspects of teaching and learning and leveraging the strengths of AI, we can create a more effective and inclusive learning environment for future generations.

Citations

Giannakos, M., Azevedo, R., Brusilovsky, P., Cukurova, M., Dimitriadis, Y., Hernandez-Leo, D., … Rienties, B. (2024). The promise and challenges of generative AI in education. Behaviour & Information Technology, 1–27. https://doi.org/10.1080/0144929X.2024.2394886

Wood, W. H., & Erasmus, D. (1904). Desiderius Erasmus concerning the Aim and Method of Education (Classics in Education ed., Vol. 19, p. 162). Columbia University Press.