Immersive Learning, AI and Improvement Science
We identified three research areas with significant potential to enhance and expand SMU capacity in TEIL and the use of technology as a research tool: (1) Immersive Learning, (2) Artificial Intelligence (AI) in education and society, and (3) Improvement Science/Design-based Implementation Research (DBIR).
Immersive learning refers to a sub-set of TEIL focused on the application of immersive and interactive technology to improve learning and facilitate behavior change. Immersive learning technologies such as augmented reality (AR), virtual reality (VR), mixed reality (MR), motion capture (MC), and game-based Learning (GBL), make learners active agents of the learning environment, often through simulation, movement, role-play, or games. Immersive learning provides important affordances for learning, including presence, epistemic agency, embodiment, and collaboration. Such affordances can allow instructors and learners to view learning experiences in novel ways, thereby disrupting traditional pedagogical structures that have kept under-served learners from experiencing high-quality instruction and enhanced knowledge and skills. In this way, immersive learning can be an avenue for improving equity and social justice. Immersive learning is also an area where recent advances in assessment can be easily applied and leveraged to areas like informal learning contexts and venues, data analytics, biosensors, and artificial intelligence.
Artificial Intelligence to enhance instruction, learning, and assessment refers to the use of automation, especially machine learning, in educational scenarios to improve learning and/or increase the scalability of instructional practices. This can include the use of cognitive agents in classrooms, the creation and improvement of AI-driven intelligent tutoring systems, AI-driven assessment of learners, and the use of AI to create more equitable and just learning experiences. The domain has become intermixed with TEIL, where new technologies provide opportunities for the use of AI through increased virtual touch points and interfaces. This increasingly requires the investigation of AI modeling that can be run on the edge to preserve learner and instructor privacy. The use of AI to improve instruction and learning will be tested in the future as a way to not only improve learning outcomes, but also accommodate the increasing learner to instructor ratios and discover pathways for teaching and learning. In the years following pandemic recovery, AI will also be poised to find disparities among learners for workforce retraining and early intervention to provide scaffolded, personalized learning experiences. Moreover, the evaluation of these technologies requires large-scale human subjects studies to inform the design and efficacy of AI-based interventions.
Improvement science for education and society refers to a methodology for continuous inquiry and learning on the part of organizations, groups, and individuals in partnership with communities for social impact. As research in TEIL expands, improvement science is poised to engage a number of faculty across SMU in the design and evaluation of these programs and systems, especially using Design-Based Implementation Research (DBIR) to provide efficient and useful feedback that informs iterative improvements. Designing effective, scalable, and sustainable policies and programs in education is challenging as programs that work in one setting or community may not work as well in others. DBIR can be a critical methodology for designing and evaluating more effective, scalable, and sustainable TEIL programs. These TEIL research areas are consistent with recent comprehensive reports that focus on the future of 21st Century educational systems and on curriculum knowledge, literacy, and skill guidelines, including OECD’s The Future of Education and Skills 2030; (http://www.oecd.org/education/2030/) and the Center for Curriculum Redesign’s Four-Dimensional Education: The Competencies Learners Need to Succeed. (https://curriculumredesign.org/our-work/four-dimensional-21st-century-education-learning-competencies-future-2030/)
Even before the pandemic, the market for technology-enhanced instruction and learning was significant, with global investments in educational technology reaching $18.66 Billion in 2019 (Business Insider, 2020). In addition, the online instructional market is expected to reach $350 Billion globally by 2025, due in large part to the introduction of flexible learning technologies and advances in artificial intelligence (ResearchandMarkets.com, 2019). Unfortunately, the benefits of the technologies described above have been unequally distributed, with the benefits going largely to the already-privileged at local, national, and global scales. How can this trend be mitigated or reversed? What approaches or resources are needed to make learning technology a force for equity and inclusion.
With the combined strengths of Dedman College, SMU Guildhall, Simmons School, and Lyle School, SMU is positioned to be a leader in research on the application of TEIL.SMU can build from its strengths and existing research projects funded by NSF, IES, NIH, and many other public and private entities. The strengths of Dedman College in cognitive, behavioral, social and data sciences in promoting learning, health and social impact, Guildhall in immersive technologies and game development, Lyle School in computer science, AI and machine learning, and Simmons School in understanding cognition, learning, teaching, and assessment, and conducting human subjects-driven research in educational scenarios, places SMU as a potential driving force in this research area. The TEIL research area addresses big questions in education and society that have applications across SMU. The questions and areas outlined above provide complex challenges that require cross-disciplinary research and collaboration.