Introduction:
The trajectory of higher education is intricately linked with the evolution of new technologies and the computational capabilities of intelligent machines. Within this realm, the progress in artificial intelligence (AI) presents both novel possibilities and challenges, potentially reshaping the governance and internal structures of institutions of higher education. However, defining the essence of artificial intelligence remains a matter of philosophical contention, with no consensus reached despite historical perspectives dating back to Aristotle.
In the 1950s, Alan Turing proposed a groundbreaking solution to the query of determining human-designed systems’ ‘intelligence.’ Turing introduced the imitation game, a test hinging on a human listener’s ability to discern between a conversation with a machine and one with another human. Successful blurring of this distinction implies the presence of an intelligent system or artificial intelligence (AI). It is noteworthy that the exploration of AI solutions dates back to the 1950s, and in 1956, John McCarthy provided one of the earliest and most influential definitions: “The study [of artificial intelligence] is to proceed on the basis of the conjecture that every aspect of learning or any other feature of intelligence can, in principle, be so precisely described that a machine can be made to simulate it” (Russell and Norvig 2010).
Understanding Artificial Intelligence in Higher Education Since 1956
Since 1956, diverse theoretical frameworks of artificial intelligence have emerged, drawing influences from fields such as chemistry, biology, linguistics, and mathematics, alongside the continual advancements in AI solutions. However, a persistent challenge lies in the ongoing dispute over the myriad definitions and interpretations. Many existing approaches tend to adopt narrow viewpoints on cognition, often overlooking the political, psychological, and philosophical dimensions inherent in the concept of intelligence.
In the context of analyzing the impact of artificial intelligence on teaching and learning in higher education, we advocate for a foundational definition, informed by a literature review of prior conceptualizations in the field. Accordingly, artificial intelligence (AI) can be defined as computing systems capable of executing human-like processes, encompassing learning, adaptation, synthesis, self-correction, and the utilization of data for intricate processing tasks.
The current trajectory of artificial intelligence is marked by rapid progression, already exerting a profound influence on services within higher education. Notably, universities are employing early forms of AI, exemplified by IBM’s supercomputer Watson. Deakin University in Australia utilizes Watson to provide student advice at any time, every day of the year (Deakin University, 2014). While Watson’s algorithms are tailored for repetitive and relatively predictable tasks, its implementation foreshadows the future impact of AI on the administrative workforce profile in higher education. The ability of a supercomputer to deliver personalized feedback round the clock diminishes the necessity for the same number of administrative staff previously assigned to this function.
Within this context, it is crucial to highlight the significance of ‘machine learning’ as a promising subfield of artificial intelligence. While some AI solutions rely on programming, others possess an inherent capability to learn patterns and make predictions. A notable example is AlphaGo, a software developed by DeepMind (Google’s AI branch), which successfully defeated the world’s best player in Go, a highly intricate board game (Gibney, 2017). We define ‘machine learning’ as a subfield encompassing software’s ability to recognize patterns, make predictions, and apply newly discovered patterns to situations not initially covered by their design.
Results and Discussion: Unveiling the Impact of Artificial Intelligence on Higher Education
The integration of Artificial Intelligence (AI) solutions holds the potential to profoundly reshape administrative services within universities. However, the landscape of teaching and learning in higher education introduces a distinct set of challenges. While AI excels at automating tasks, its application to the more intricate aspects of higher learning remains a complex endeavor. Notably, supercomputers face challenges in discerning nuances such as irony, sarcasm, and humor, often resorting to superficial solutions based on algorithms that analyze factors like punctuation marks, capitalization, or key phrases (Tsur et al., 2010).
Despite the current hype surrounding the possibilities of AI in education, it is imperative to acknowledge the realistic limitations of AI algorithmic solutions in the multifaceted realm of higher education. Examples from other domains, such as the tragic incident in 2016 involving a self-driving car on ‘autopilot’ and Microsoft’s misstep with the AI-powered bot named Tay on Twitter, underscore the importance of understanding AI’s current constraints and potential risks.
While AI solutions undeniably open new horizons for teaching and learning, they are not positioned to replace teachers outright. Instead, they present a viable opportunity for augmentation. As computing algorithms increasingly infiltrate daily life, impacting aspects from credit scores to employability, higher education finds itself at the epicenter of this transformative shift. The consequential changes in service quality, temporal dynamics within universities, and workforce structures necessitate careful consideration and analysis.
The convergence of significant advances in machine learning and artificial intelligence introduces both extraordinary opportunities and risks to higher education. As the landscape evolves, it becomes essential to recognize that education is fundamentally a human-centric endeavor rather than a technology-centric solution. Despite the rapid strides in AI, reliance solely on technology is perilous, and attention must be maintained on human agency in identifying problems, critiquing, addressing risks, and posing crucial questions.
The paper emphasizes that the real potential of technology in higher education lies in extending human capabilities and possibilities in teaching, learning, and research. It advocates for scholarly discussions that align with ambitious research agendas, such as the “National Artificial Intelligence Research and Development Strategic Plan” released by former US President Barack Obama in 2016. The report highlights the gradual erosion of walls between humans and AI systems, emphasizing the need for fundamental research to facilitate effective human-AI interaction and collaboration (U.S. National Science and Technology Council 2016).
As AI continues to progress, it is essential for educational institutions to vigilantly navigate the potential monopolization of control over hidden algorithms by tech giants. The paper warns against the dangerous lack of transparency in algorithms and their usage, cautioning against unchecked power that can influence every facet of contemporary society. The monopolistic control over information by tech corporations contradicts the democratic ethos of higher learning and stifles dissent and diverse perspectives.
Simultaneously, the accelerated advancements in AI coincide with the financial struggles of defunded universities, seeking economic solutions to offset budget depletions. AI’s capacity to replace administrative staff and teaching assistants prompts an exploration of its effects on learning in higher education, especially considering the growing demand for initiative, creativity, and entrepreneurial spirit among graduates.
The Rise of Artificial Intelligence and Augmentation in Higher Education
The landscape of learning and teaching has undergone rapid transformation over the past three decades with the introduction and assimilation of new technologies. Today, as we navigate through the current technological milieu, it’s easy to overlook the fervent debates that once raged in educational institutions concerning the use of what are now considered basic technologies. A longitudinal study spanning from 1993 to 2005 in the USA, focused on accommodations for students with disabilities, vividly reminds us of the contentious debates surrounding the utilization of calculators and spell check programs – technologies now commonplace but once embroiled in controversy (Lazarus et al., 2008).
Originally developed to aid individuals with disabilities, technologies like text-to-speech, speech-to-text, zoom capabilities, predictive text, spell checkers, and search engines have evolved into standard features found in personal computers, handheld devices, and wearable gadgets. These tools have surpassed their initial functions, now enhancing the learning experiences of students globally and expanding the potential for teaching and designing educational content.
Moreover, artificial intelligence (AI) has seamlessly integrated into the tools and infrastructure of cities and campuses worldwide. From Internet search engines and smartphone features to public transportation and household appliances, AI has become a ubiquitous presence in our daily lives. Take, for example, iPhone’s Siri, which operates through a sophisticated set of algorithms and software, showcasing how AI solutions have become ingrained in our routines (Bostrom and Yudkowsky, 2011; Luckin, 2017). While Siri may be perceived as a relatively simple AI solution or merely a voice-controlled interface, its roots as an AI project funded by the Defense Advanced Research Projects Agency (DARPA) since 2001 underscore its significant evolution (Bostrom, 2006).
The impact of AI extends across various domains, including personalized solutions such as the partnership between Talkspace and IBM’s Watson for AI-assisted psychotherapy (Rutkin, 2015). As AI becomes more integrated into our daily lives, students are encountering a myriad of opportunities and challenges in higher education.
Human-machine interfaces, exploring the amalgamation of human capacities with technology, present unprecedented potential to reshape learning, memory, access to information, and information creation. The concept of ‘cyborgs’—a crossbreed of human and machine— is no longer confined to science fiction, as demonstrated by ongoing developments in technologically advanced prosthetics, exoskeletons and interfaces that close the loop between human and machine (De Lange 2015). This rapid progression in computing systems, driven by machine learning algorithms, extends the capabilities of individuals, engaging in human-like processes and complex tasks, offering a glimpse into a transformative era for higher education.
As AI’s impact becomes increasingly apparent in the global economy, major tech corporations such as Google, Apple, Microsoft, and Facebook are making substantial investments in AI research and applications. Google’s recent announcement regarding the utilization of its quantum computer, D-Wave 2X, for handling complex AI operations represents a significant milestone (Neven, 2015). This surge of interest and investment in AI is anticipated to have a profound influence on universities, driven notably by financial pressures and the burgeoning student population. The prospect of leveraging intelligent machines to supplement or even replace aspects of the academic workforce becomes increasingly feasible, particularly amidst the emphasis on cost reduction and the prevalence of casual and short-term contracts within academia (Grove, 2015).
The emergence of AI presents educators with the challenge of exploring new dimensions, functions, and pedagogies within an ever-evolving landscape of learning and teaching. Advancements such as brain-computer interfaces (BCIs), which have made significant strides, offer viable solutions for enabling remote software control and communication for individuals with motor function disabilities (Andrea et al., 2015; Wolpaw and Wolpaw, 2012; Kübler et al., 2015). The rapid pace of technological evolution prompts us to ponder how soon human-machine interfaces will enhance human memory and cognition.
As we navigate the intersection of AI integration in higher education, the potential for AI to augment human capabilities presents a multitude of opportunities and challenges. The ongoing innovation in education transcends mere technological integration in classrooms; it necessitates a fundamental overhaul of teaching methodologies to empower students with the skills requisite for success in a globally competitive environment (Schleicher, 2015). The evolving notion of ‘cyborgs’ and the integration of AI functionalities into our daily lives compel us to contemplate the future of teaching, research, and the essence of humanity within academia.
Past Lessons, Possibilities, and Challenges of AI Solutions
The imperative to enhance inclusivity in higher education, combined with the challenges of increasing enrollment numbers, growing class sizes, rising staff costs, and broader financial pressures on universities, has led to a growing interest in adopting technology or teacherbots. This inclination became evident with the rise of massive open online courses (MOOCs), which captured the attention of many university administrators. MOOCs, known for their ‘open’ nature with no entry requirements or fees, offered opportunities for global enrollment. However, despite their potential, MOOCs presented challenges for teachers in managing diverse students on a global scale, highlighting the limitations of these ambitious endeavors.
Reflecting on the lessons gleaned from MOOCs is imperative. Popenici and Kerr caution against viewing MOOCs as heralds of significant change in higher education, likening them to mere shells on the seabed rather than the transformative wave some anticipated (Popenici and Kerr, 2013). The disappointment stemming from MOOCs’ inability to fulfill their promises and the unwarranted hype surrounding them serves as a reminder of the perils of blindly embracing technological trends without evidence-based considerations.
Reflecting on past experiences, it becomes clear that the true catalyst for change in higher education lies in the impact of machine learning. It is imperative to learn from the missteps of past fads and resist the temptation to embrace convenient complacency. While online learning shows potential benefits for institutions, it should not overshadow the importance of adopting a diverse and evidence-based approach. MOOCs, in particular, served as a stark reminder of the risks associated with solely relying on one technological solution.
As higher education delves into the uncharted territory of AI’s potential in teaching, learning, and organizational governance, glimpses of both implications and potentials begin to emerge. Breakthroughs in non-invasive brain-computer interfaces and AI present opportunities to reconsider the role of educators or even contemplate the integration of virtual “teacherbots” (Bayne, 2015; Botrel et al., 2015). Technologies like brain-computer interfaces offer the capability to measure student engagement, while advanced supercomputers like IBM’s Watson have the potential to automate teacher presence throughout a course.
The emergence of teacherbots, exemplified by cases like Jill Watson in Georgia Tech’s online Master in Computer Sciences program, represents a disruptive alternative to traditional teaching assistants (Maderer, 2016). However, caution is necessary when contemplating the replacement of human educators with algorithmic solutions. While AI has advanced to the point where it can serve as a personalized tutor, the implications for the future of teaching are profound. Despite AI’s rapid progress, the complexity of the human mind remains unparalleled, and at present, AI cannot fully replicate human intuition and understanding.
The current landscape urges policymakers and experts to reimagine higher education institutions within a new paradigm, one that prioritizes imagination, creativity and civic engagement. While AI has the potential to personalize learning experiences and engage students, it is crucial to avoid reducing education solely to the pursuit of ’employability.’ While teacherbots may challenge traditional teaching staff, their implementation should align with a broader reconsideration of ‘graduate attributes’ and the core values of higher education.
As the capabilities of teacherbots continue to be explored, the pervasive presence of AI in our daily lives underscores the need for a more targeted research agenda in higher education. AI solutions are already deeply ingrained in various aspects of our existence, managing choices, preferences, and providing feedback. Teacherbots, defined as algorithmic interfaces leveraging artificial intelligence for personalized education, represent a tangible application of AI in teaching and learning. They have the potential to offer tailored content, supervision, and guidance, thereby reshaping the dynamics of online learning environments. The integration of intelligent machines into education signals a departure from traditional modes of information dissemination towards a more personalized, scalable, and cost-effective alternative, as demonstrated by entities like ‘Jill Watson.’ While acknowledging the importance of faculty-led instruction, intelligent machines can effectively address the learning and support requirements of a large student body.
Conclusion: Navigating the Future of AI in Higher Education
The rise of Artificial Intelligence (AI) prompts a deep examination of its forthcoming impact on teaching and learning within higher education. The swift advancement of technological innovations, alongside the undeniable job displacements linked to it, underscores the need for a reassessment of the roles of educators and pedagogical methods in higher education (source). Present implementations of technological solutions, such as ‘learning management systems’ and IT tools designed to counter plagiarism, invite a critical investigation into the control exerted over teaching agendas—whether by corporate entities or institutions of higher learning.
As tech giants ascend to prominence, concerns over privacy and the potential emergence of a dystopian future cast a shadow over the horizon. These pressing issues require special attention, compelling universities to integrate them into their risk assessment frameworks when charting a sustainable path forward.
Furthermore, AI, propelled by complex algorithms crafted by programmers, poses a threat to replace many foundational tasks inherent in current teaching methodologies. The potential biases or concealed agendas embedded within these algorithms underscore the importance of ongoing scrutiny of proposed solutions, ensuring that universities maintain their role as bastions of civilization. They must continue to nurture the cultivation and dissemination of knowledge and wisdom while upholding ethical standards and preserving inclusivity.
The current moment demands that universities reevaluate their functions, pedagogical models, and relationships with AI solutions and their developers. As we contemplate the myriad possibilities and challenges inherent in integrating AI into teaching and learning, institutions of higher education stand on the brink of a transformative era. These AI solutions not only pave the way for universal education but also reinforce lifelong learning within a framework that upholds the integrity of core values and the intrinsic purpose of higher education.
Recognizing the imperative for research, we advocate for a comprehensive exploration of the ethical implications associated with the current dominance of a select few entities in shaping AI developments. Concurrently, there is a pressing need for intensified research focusing on the evolving roles of educators within novel learning pathways tailored for advanced degree students. This envisions a fresh set of graduate attributes that prioritize imagination, creativity, and innovation—qualities inherently human and resistant to replication by machines. At this juncture, it is our collective responsibility to guide the integration of AI in higher education, mindful of its ethical dimensions, and to chart a course that preserves the essence of human intellect and creativity.
SSRI
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