Extended Reality (XR) Teaching in the Era of Deepfakes: A TPACK and LOU Primer for Filtering Deepfakes and Malinformation in Subject-Area Content

Introduction

The use of existing and emerging technologies in teaching and learning provides the opportunity to present subject-area content using devices, programs, and venues in ways that promote higher-order thinking and long-term retention. In the last decade, advances in Artificial Intelligence (AI) have resulted in the development of virtual reality programs that enable end-users to interact with content in third and fourth dimensional interactive spaces (i.e. Extended Reality or XR). The transformation beyond traditional face-to-face or two-dimensional teaching and learning, while it has provided unique modalities to not only deliver instructional content but also actualize student learning outcomes, has resulted in an unforeseen digital side effect. The digital side effect presents in the form of deepfakes or the deliberate alteration of audio and visual content to advance a specific point of view. Typically, deepfake content is characterized by the dissemination of malinformation in teaching and learning which can negatively impact desired student learning outcomes. Thus, the premise of this chapter is to present a primer using the Technological, Pedagogical, and Content Knowledge (TPACK, Mishra & Koehler, 2006) and Levels of Use (Hall, et al., 1978) construct contextualized within Vygotskian (1978) object/other to self-regulation Sociocultural Theory (SCT) to mitigate the presence of deepfake and malinformation in subject-area content when working in XR environments.

Teaching and learning methods and modalities in the 21^st century continue to evolve as technologies change. As such, today’s learner expects their learning environment to reflect the technologies they have adopted in their everyday lives. Educators, then, are charged with ensuring that subject-area content is delivered using a variety of technology-infused and content-relevant instructional strategies that will keep learners engaged while ensuring learning outcomes produce higher-order thinking and long-term retention. As more digitally literate learners enter colleges, schools, and universities, they expect teaching faculty and content-related programing to reflect a digitally rich environment as the technologies they use evolve and change. These digitally native learners crave interactions and simulations that mirror activities in daily life, and this is evinced by the increase in the use of Extended Reality programs that allow educators to reimagine teaching content in virtual spaces. This is particularly noted with the rise in use, especially in the last decade of programs driven by artificial intelligence (AI) that have cross-platform operability (Prensky, 2010; Horizon Report, 2021). As Rosen (2010) pointed out, there has not been a study conducted to suggest if or how teachers are using virtually based programs, such as popular social media sites, to supplement their training that they perceive as non-responsive to their preferred “anytime, anywhere” learning preferences. Therefore, in considering the technical skill set of the 21st-century teacher, teacher education faculty must respond in kind by filtering their existing face-to-face models through a model such as the Technological, Pedagogical, and Content Knowledge (TPACK) and Levels of Use (LoU) framework (Mishra & Koehler, 2006) to enable the Zone of Proximal Development (ZPD) to open and salient, higher-order learning can occur among learners (Vygotsky, 1978). External to the educational setting, many learners are regular users of XR platforms to enrich their learning experiences beyond traditional teaching and learning modalities, especially those that are technology-driven. If the efficacy of using XR is filtered through the TPACK and LoU filtered through an SCT lens, not only will this facilitate digital competency and literacy among educators but also provide additional support structures to mitigate deepfake and malinformation from negatively impacting content.