As post-secondary institutions (PSIs) look to data and machine learning (ML) to improve the education experience, they may be working toward a future where artificial intelligence (AI) can create substantial improvements in equity and accessibility within teaching and learning in higher education. Inequity and inaccessibility may be caused by many factors — such as race, sociocultural differences, financial disparity, disabilities, and geography (ABLE Research Consultants, 2020; Kahu & Nelson, 2018; Scheurich et al., 2017) — but at its heart, equity requires that a student’s circumstances and background does not keep them from reaching their academic goals (ABLE Research Consultants, 2020). Removing these barriers may prove easier for AI to accomplish than a human instructor and powerful tools are already assisting in this task. For example, Blackboard Ally (https://ally.ac/) is a tool that provides course content in accessible formats and helps teachers to provide accessible content within their learning management system (LMS) (Blackboard Incorporated, n.d.), and AI chatbots are helping students when human support is unavailable (Goldweic, 2019). Furthermore, there are tools that automatically adapt curriculum by assessing knowledge gaps and customizing coursework to address the needs of each student (Knewton, 2018). Accordingly, as higher education adopts new models that prioritize flexible learning pathways, individualized curriculum, and AI-powered communication, imbalances within educational experiences may be all but eradicated.

Barriers for students in post-secondary education have a long and complex history, but governments and organizations are making their removal a priority (Brown et al., 2020; European Commission, 2020), and by 2030 public pressure will have caused these priorities to become a core element of how PSIs receive government funding. Reductions in government funding have been an ongoing issue for PSIs through the 2010s (Jacob & Gokbel, 2018), and some governments are shifting to performance-based funding (PBF) models, which provide funding based on the analysis of key metrics like post-graduation employment, graduation rates, and graduating low-income students (Ortagus et al., 2020; Spooner, 2019). By 2030, these funding models will continue to focus heavily on performance metrics, but increased lobbying from organizations for governments to prioritize social justice will result in more impactful PBF equity metrics relating to income, racial/ethnic minorities, and accessibility. The data-gathering capabilities required to enable institutions to measure the necessary analytics will fuel a growth of student data tracking solutions that institutions will connect directly with their learning management systems (LMS).

The metrics required by new government funding models will lead PSIs to rethink existing educational methods in order to facilitate accessibility for learners who may have had difficulty accessing face-to-face educational schedules. Already, PSIs are looking into automation and modular programming as a means of long-term cost reduction (Monat & Gannon, 2018), and the metrics required for PBF will further facilitate the move to a modular approach to course structure. Over time, the structures of higher education will be broken down and rebuilt to support modular programming, such as industry-verified micro-credentials, which could be acquired in a flexible time frame (Bonfield et al., 2020; Czerniewicz, 2018), providing adult learners the opportunity to acquire accreditation as their schedule allows. Furthermore, this freedom and flexibility will help those with difficult schedules, poor internet connections, or other accessibility issues to acquire their education at their own pace without fear that these barriers are keeping them from gaining necessary skills to begin, move forward, or pivot in their career. This shift to modular learning will require institutions to take a nimble approach to the needs of students who are no longer learning at pre-determined times but are potentially beginning and completing their education at any point throughout the year, necessitating the use of AI to respond to, and predict, the needs of every student.

As the use of modular courses grows, institutions will use machine learning to predict and adapt course pathways based on students’ needs to facilitate an ideal online learning environment that maximizes every student’s potential to reach their desired educational goals. Since each student has a unique sociocultural story and educational background, these will need to be considered as the AI learns from gathered data and other student activity to develop a unique path toward their educational success. Through timely feedback, insightful progress dashboards, consistent progress communication, automated grading, and seamless adjustments to optimize pedagogical approach (Macgilchrist, 2018), every student will be offered an education that has been tailored to their needs. Additionally, AI chatbots that once only handled tasks like answering basic student questions (Goldweic, 2019; Leeds Beckett University, 2017) will use machine learning to develop responses to questions relating to complex course content, with human support being required only when technical issues or novel concepts that AI has yet to learn are uncovered. This means that while AI is to be responsible for day-to-day activities that were previously the task of an instructor, course curriculum would be developed and updated by subject-matter experts, instructional designers, and experts in sociocultural teaching approaches. By employing human experts to develop diverse, culturally sensitive curriculum, and training educational AIs with a focus on equity, educational roadblocks like cultural ignorance, racial discrimination in grading, or linguistic barriers will be nearly eliminated from higher education.

Through this AI-powered revolution, students will have an educational experience that respects their unique story and optimizes itself to ensure their success, but this will introduce numerous questions about the purpose of higher education in society. It cannot be denied that systemic barriers and biases that favour those in dominant groups limit access to quality education for the marginalized, and that online learning environments provide an effective way to address these inequities (ABLE Research Consultants, 2020). However, does the shift to individualized, AI-curated curriculum reinforce the idea that higher education can be treated as a commodity to be purchased like a Netflix subscription? Despite making up much-needed headway against educational inequities, will the move away from teacher-led instruction reduce higher education’s role in challenging preconceptions of students and preparing them for the humanity of a career they may have no experience with? As Pasquerella (2019) said, we “must adopt an equity-minded approach by being intentional about connecting curricula to careers, paying attention to reducing the costs for students, and positioning graduates for success in work, citizenship, and life by promoting student agency” (p. 3). This cannot be accomplished if students continue to be faced with biased educational approaches, insensitivity toward sociocultural differences, and linguistic barriers that obstruct their understanding of course content. Though the future of education will face countless challenges and existential crises, these must be wrestled with and philosophized over on the path to a more equitable world for the marginalized.

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