Just as the invention of the printing press did in 1436, the commercial evolution of the internet in the 1980s and launch of the World Wide Web in 1993 transformed the sharing of knowledge, the introduction of GenAI/LLMs has the potential to radically overturn the existing monopoly on knowledge sharing and expertise, enabling more learners to both access and gain value from information and knowledge. To date, however, access to and success in higher education remains highly correlated with nonacademic factors such as race, family income and location. This lack of access is often justified through a scarcity mindset based on an argument of limited resources including lack of space at current institutions, lack of suitable locations, resources and modalities of operation for new institutions, insufficient well-qualified faculty and staff, and the consequent inability to provide multiple pathways for learners both into the system and to gaining value through subsequent onramps to careers that enable socioeconomic advancement.
These scarcity-based justifications not only restrict opportunities for students who might benefit the most but also perpetuate inequalities in the attainment of knowledge and skills and constrain learners’ ability to gain socioeconomic mobility and consequently regional ability to enhance economic prosperity based on the level of workforce-related intellectual capital. Recent technological advances, particularly in generative AI, offer promising mechanisms for dismantling the scarcity mindset and removing barriers that disproportionately affect those historically sidelined.
Scarcity of Access
The traditional model of higher education based on physical campuses and face-to-face instruction inherently limits access. Over decades, it has been controlled through barriers for admission such as selectivity based on highly debated criteria of past performance, price and even location. While aspects such as space, availability of residential options, class-size and instructor-student ratios are meaningful criteria, it must be understood that each is based on a model of education and modalities of knowledge sharing established by the realities and norms of past centuries, rather than the opportunities and avenues presented in a technologically enabled world. One must, at this juncture, ask whether higher education’s focus is to constrain knowledge or to bring the essence of teaching and learning, knowledge acquisition, to a much broader audience.
Technology can enable higher ed to bring education to the learner through digital/online means, rather than forcing the learner—and the expert—to be geographically colocated with the institution, thereby substantially enhancing reach and the ability to provide access. AI builds on this access, presenting new ways of expanding it to aspects of teaching and learning—like adaptive and personalized learning and the availability of 24/7 tutors as well as instruction in multiple languages—and using different contexts to best fit learner needs, enhancing scale of operations without the prior constraints of physical space and even time. This work extends to other aspects such as student services, including health and wellness, and even improves processes related to enrollment, finance, facilities management and IT. The efficiency gain and ability to further develop the intelligent and accessible systems AI presents can enable institutions to grow capacity multifold without decreasing quality. The emergence of phygital campuses integrating both physical and virtual functionalities and modalities removes past barriers while still enabling learners to benefit from rich experiences regardless of location. Incorporating intelligent learning management systems can further this accessibility at scale by enabling personalization and reducing the dependence on physical locations for engagement and services. Advances in AI and digital technologies can ensure that access to an elite level of education does not have to result from elitism and that knowledge can be gained without the constraints of time, space and location. It is emphasized, though, that fulfilling this mission necessitates a significant shift from the residential, physical classroom and restricted time of operation modes used to date to those that are far more flexible, focused on the learner and their needs and convenience, rather than those of the institution.
Scarcity of Location
The current model of education has largely required student proximity to the institution, with geographical boundaries often acting as barriers to specific programs and even institutions. Beyond the immediate consequences to individuals and families from heretofore underserved populations—those with limited mobility and those living in rural areas—the scarcity of location also results in areas remaining poor due to a lack of sufficient levels of human capital to expand commerce and technology, which in turn would improve the socioeconomic conditions of those regions. Lack of financial resources to build campuses closer to groups of learners and our inability to rapidly change curricula and structures to meet changing demographic and workforce needs also make it difficult to establish traditional physical campuses.
In addition to the growth of online and blended programs, innovations in augmented (AR), virtual (VR), and extended reality (XR) over the last few years, have opened promising avenues for learning and training through virtual immersion often at levels of engagement and depths not possible in face-to-face interactions, let alone at scale. The use of AI tutors, guides and mentors at the individual and group levels can foster a sense of community and engagement irrespective of the learner’s location. It becomes possible to avoid disparities in access and attainment that location constraints create. Using AI to reduce scarcity of location can truly enable talent to meet opportunity nationwide. In addition to simulated classroom environments engaging individuals and or groups of noncollocated students, the combination of AI with AR/VR and XR technologies allows for direct immersion such as virtual visits to museums and art galleries, exploration of archeological digs and even immersion in social and cultural contexts that are far from those around the individual learner but still reachable through appropriately designed technology. Entire groups of learners can be transported to virtual simulations of history and culture, enabling learning and sociocultural engagement at scale at levels heretofore unimaginable without travel. When combined with extension and learning support centers, the scale of engagement can be expanded significantly without location being a barrier. Again, it necessitates a change in focus, not just with modality of operation but also in assessing competency, moving from an outdated time-in-seat basis to one of demonstrating mastery through multiple paths.
Scarcity of Experts
In addition to space and resources, the shortage of experts places a limit on enrollments and access to knowledge. Most often this is also the rationale for insisting on the use of a for-credit modality rather than one of continuous learning. In addition to online and blended programs, AI tools can further simulate the instruction and mentorship seasoned educators provide, delivering multilingual and culturally responsive instruction and engagement. Beyond matching instructor expertise, these platforms are accessible 24/7 and can be tailored to meet the needs of diverse academic levels and sociocultural backgrounds. They can offer consistent, high-quality experiences, vastly expanding reach, identifying and adapting to how students approach and solve problems, overcoming educational barriers and building on student strengths. In addition to multiplying the number of experts, they can also serve as highly skilled personal assistants for instructors, assisting in routine and repetitive tasks to give instructors more time and focus on the primary mission to help learners acquire and understand knowledge. AI-enabled platforms can also analyze learner interests and potential workforce needs to match the learner with relevant experts around the world, so they can gain from experts even in niche areas and disciplines where local experts may not be available. Such advances can amplify learning and access at a scale far beyond what’s currently available, without being constrained by locally available expertise and capacity.
Scarcity of Pathways
The predominance of a single pathway—the degree—can be a significant constraint for an increasing number of learners, for many of whom a continuous focus on education is impossible due to other ongoing responsibilities necessitating modalities that would provide a carousel, with the learner stepping on and off as needed, gaining credentials along the journey to access better jobs, greater resources and increased security along the path. AI-driven platforms make offering multiple options easier, effectively merging the previously distinct roles of degree-based education with those of individual courses and certificates offered directly or as part of continuing and professional education, and in doing so also encourage the move from standardization to personalization with inbuilt flexibility to meet new and changing needs. Simultaneously, in the context of millions of employees with some or no college—and no degree—academia can now play an increased role in facilitating the continued employability of people already in the workforce through short-term credentials and certifications, allowing them to update their knowledge and skills base and thereby enabling greater socioeconomic mobility.
In similar vein, selecting a pathway to a career can be extremely challenging in a traditional academic environment. AI-enabled platforms can provide tremendous assistance here by analyzing student data and interests and matching them with potential workforce needs, providing guidance and advising that is highly personalized and timely, which increases the chances of success. It would also allow learners to explore multiple career pathways, gaining from immersive work-study experiences and even remotes internships and externships. AI thus expands on the choices available to learners, not just in terms of courses and credentials but also in career exploration and the ability to meld periods of study with those of work, enhancing socioeconomic mobility along the path, rather than just at the end.
The scarcity mindset, built on the pervasive belief that resources and opportunities are limited, has been strengthened over the years by higher education’s focus on outdated structures and modalities of interaction and engagement. While this mindset has changed in recent years at institutions such as WGU, SNHU and ASU, overcoming the challenges perpetuated by a system built on a foundation of exclusivity rather than access, motivation and merit, appears daunting. While advances in AI and other technologies are not the panacea to higher education’s history of exclusion and poor outcomes, they can be a means of changing processes and policy for the better. They can help redefine the existing paradigm, offering the potential of personalized attention and resources to thousands of additional learners, irrespective of location, socioeconomic status and background, enabling greater levels of learning and career and life success at scale.
Democratizing access to knowledge and learning is an age-old commitment that higher education has failed to keep. There are now mechanisms that force reflection on and re-evaluation of how higher education meets its promise of being the great equalizer and ensuring the activation of talent to opportunity at scale. As AI use continues to evolve, the higher ed community needs to embrace it thoughtfully and responsibly, paving the way for a future where learning is enabled at scale while also being flexible and personalized, breaking the prevalent scarcity mindset and changing it to one of abundance to enable access and opportunity for all. The question is whether we are willing to make that leap.
You Might Also Like
