Artificial Intelligence and The Future of Higher Education

Artificial intelligence & the future of education systems | Bernhard Schindlholzer | TEDxFHKufstein

"Disruptions are always jarring – and the leap in #AI capability is certainly a disruption

– but disruptions are also opportunities." - McGraw Hill (2023).

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Introduction

Schindlholzer refers to Fuller's Theory of Ephemeralization (TEDx Talks, 2016). According to Fuller, technology, and engineering would make it exponentially possible to do more with fewer resources (Buckminster Fuller and Systems Theory, n.d.). Is Artificial Intelligence the next step in this process?

Artificial intelligence is a disruptive technology bringing change to every industry (Rouhiainen, 2019). AI can be any tool that uses association-based automation (U.S. Department of Education, 2023). In higher learning, some of these tools include course assistants (Hill, 2023), deep learning (Yang et al., 2021), neural networks (Xie et al., 2021), chatbots, and virtual assistants (National Louis University, 2023). The role of higher learning is to best prepare learners for the future workplace. AI alters the tasks and comprehension needed in the modern workplace (Ramos, 2022). Socio-economic skills will take priority over digital skills (Samek et al., 2021). Ultimately, everyone in education is responsible for the best implementation of this technology while safeguarding against the dangers it may impose (U.S. Department of Education, 2023). Much of the research on these technologies is developing. Recent application of these technologies limits empirical data and introduce speculation. This case study will cover AI through the lens of learner motivation. The current applications, hopes, practical uses, and potential dangers.

"Our approach to teaching should be guided not by one recent product but by reflection on the lives our students are likely to lead in the 2030s" - Susan D’Agostino (2023)

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The Shift to Increase Learning Meaning

Schindlholzer recommends Higher education shift from pure knowledge transfer to problem-based learning environments (TEDx Talks, 2016). Cultivating cognitive thinking and problem-solving skills in learners (Yang, 2021). Knowledge perceived as meaningful to learners promotes motivation to learn (Keller & Diemann, 2018). Problem-based learning environments focus on knowledge application and reflection (TEDx Talks, 2016). By introducing meaningful challenges, instructors create a sense of authenticity for the learner (Reiser & Dempsey, 2018). These challenges encourage iterations and evaluation of course content (TEDx Talks, 2016). Similar to the Socratic method, instructors leave the discussion open-ended and take part in the learning as a guide (Reis, 2003). This method develops a greater sense of meaning for the learners (Reis, 2003). Second, instructors should encourage AI literacy (D’Agostino, 2023). AI can enhance outcomes and efficiency for the learner (D’Agostino, 2023). Limitations of AI require the learner to rely on their abilities to complete a task (D’Agostino, 2023). In some cases, students favored their intellectual abilities over the use of generative AI (D’Agostino, 2023). Rouhiainen (2019), notes the combination of human intelligence with Artificial intelligence will bring the best result. A combination of problem-based learning environments with AI literacy fosters meaning. Equipping learners with skills needed for future workplaces. Including creativity, problem-solving, teamwork, and communication (Ramos, 2022). Furthering learners' sense of competency. Which leads to positive engagement, and learning motivation (Reiser & Dempsey, 2018).

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Personalized Learning for Satisfying Outcomes

Immersion in problem-based environments allows learners to focus on long-term goals (TEDx Talks, 2016). The ability to prioritize long-term goals increases self-efficacy in learners (Reiser & Dempsey, 2018). Simulation affords students a safe place to fail (TEDx Talks, 2016). Simultaneously, allowing the instructor to analyze their ability to apply course knowledge (TEDx Talks, 2016). AI increases the ability to customize and create equitable curricula (U.S. Department of Education, 2023) to combat learned helplessness (Rieser & Dempsey, 2018). According to Keller and Diemann (2018), learner anticipation of satisfying outcomes promotes motivation. Course assistants like ALEKS use Deep Learning to create a natural flow of content for the learner (Hill, 2023). Using Knowledge Space Theory (Hill, 2023), these tools structure course content to give learners more control of their learning pathway (Oliveira, 2021). Yang et al. (2021), refer to this practice as precision education. Precision education adapts and personalizes content for each learner (Yang et al., 2021). Precision education promotes successful outcomes through a self-paced learning environment (Yang et al., 2021). According to the Expectation-Value theory, motivation is highest when a learner is challenged and believes the goal is attainable (Spott, 2022). Precision education delivers new challenges and feedback for learners based on prior success (Yang et al., 2021). While allowing instructors to enhance learner experience through supplemental delivery and positive feedback (Yang et al., 2021). Reiser and Dempsey (2018), recommend using feedback that reinforces the success of these challenges through congratulatory comments.

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Expectancy-Value Theory (Penk & Richter, 2016)

Artificial Intelligence and the Future of Higher Education | Global Silicon Valley

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Deep Learning for Student Volition

The process of learning is rarely linear as the complex nature of goal relationships change (Reiser & Dempsey, 2018). The affective system of a learner, which includes their emotional state and well-being, influences academic engagement (Acosta-Gonzaga, 2023). Reiser and Dempsey (2018), note the prescriptive nature of volition requires comprehension of the learner, inspection of their motivation, and the evaluation of treatment. Long-term learner engagement is significant to academic success (Li et al., 2023). Deep Learning assesses patterns beyond academic progress and can identify at-risk students (Hill, 2023). These tools can predict learner attrition and inform instructors how to influence volition (Yang et al., 2021). Empowering instructors to intervene with at-risk learners earlier than before (Yang et al., 2021). AI tools enable instructors to adjust learner outcomes to overcome curriculum attrition (Rouhiainen, 2019). Built on real interactions these large datasets, complimented by AI, provide insight for instructors (Global Silicon Valley, 2023). Further research suggests generative AI enables higher education institutions to further positive learner engagement (Global Silicon Valley, 2023). Supplementing current or limited support structures (Rouhiainen, 2019). Chatbots and virtual assistants improve student motivation and mental well-being (Rouhiainen, 2019). Combating learner attrition (Global Silicon Valley, 2023), while influencing positive change, promotes self-regulation for students (Reiser & Dempsey, 2018). Allowing learners to maintain motivation (Keller & Deimann, 2018) and achieve long-term goals (Reiser & Dempsey, 2018).

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Potential Dangers

Energy

The future is here (TEDx Talks, 2016), and Artificial Intelligence provides several positive applications. But, there are some dangers that we must regard during its implementation. Fuller's Theory of Ephemeralization was born in 1938 from the anticipation of increasing population and decreasing natural resources (Buckminster Fuller and Systems Theory, n.d.). AI will give the end-user the ability to accomplish more with tangibly less (TEDx Talks, 2016). Consequently, the computing power required to run these systems require global resources. By 2030, these systems estimate to use 20% of the planet's energy supply (Yang et al., 2021). Overapplication could lead to a worldwide energy crisis (Yang et al., 2021).

Equity

AI tools can provide greater access and equity for learners (D’Agostino, 2023). However, biases in training sets can create social inequalities (Yang et al., 2021). Similarly, data biases can negatively affect cultural value systems, and principles, and potentially violate human rights (Yang et al., 2021). Algorithmic bias and voice recognition limitations can create unintentional discrimination against learners (U.S. Department of Education, 2023).

Misinformation + Privacy

Generative AI models can provide incorrect information and need oversight (Rouhiainen, 2019). Chatbots at National Louis University (2023), can answer common support questions with 91% accuracy. These learning sets also require personal data which must be monitored and protected (Rouhiainen, 2019). Transparency of tool providers and the need for greater surveillance provide inherent privacy concerns (U.S. Department of Education, 2023).

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Conclusion

In summary, Artificial Intelligence gives instructors greater insight and tools to positively engage learners. Problem-based learning environments promote high-demand future skills (TEDx Talks, 2016). While building motivation through relevance and knowledge application (Keller & Diemann, 2018). AI literacy and regulation promote learner competency (D’Agostino, 2023). Precision education provides real-time feedback (Yang et al., 2021) and student control over their learning journey (Oliveira, 2021). Increasing learning motivation through the anticipation of successful outcomes (Keller & Diemann, 2018). Deep Learning and Neural Networks can assess patterns, identify at-risk students, and prescribe instructor treatment (Yang et al., 2021). Chatbots and Virtual assistants can supplement support structures and promote student well-being (Rouhiainen, 2019). All of which enable learners to maintain motivation through self-regulation (Keller & Diemann, 2018). Ultimately, everyone in education is responsible for the best implementation of this technology while safeguarding against the dangers it may impose (U.S. Department of Education, 2023). Higher education should adapt learning environments, and implement and increase AI literacy in their institutions. Not in response to new tools, but through reflection of learner futures in the decades to come (D’Agostino, 2023). In order to best prepare learners for the future workplace.

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References

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Thomas Boldman. June 14, 2023