artificial intelligence – technology driven pedagogy

Knowledge acquisition evolves from the environmental and technological influences on the presented information. Artificial intelligence (AI) uses machine-based learning to accomplish tasks and activities that have historically relied on learner’s cognition (Alexander, Ashford-Rowe, Barajas-Murphy, Dobbin, Knott, McCormack, Pomerantz, Seilhamer, & Weber, 2019, p. 27). Like original computer-based learning theories, AI uses computer programming to navigate advanced algorithms to predict and measure human task completions and decision-making (Alexander et al., 2019, p. 27). Learning institutions and change-based organizations have implicated AI into the facilitation of knowledge. AI, a tool to support instructors, has not been well received by the masses, hindered by privacy and ethical concerns (Gimbel, 2018).

AI has increasingly adapted to modern society, with seventy-four percent of Americans’ integration perception being positive (Gimbel, 2018). AI has many promising implications on instructional facilitation, time-constraints, and the ability to scale instruction while maintaining a personalized approach to the Learner (Brooks, 2018). Innovations such as Apple’s iPhone’s FaceID, Amazon’s Alexan, Google Assistant, and many variations of interactive chatbots all exist as commonly used AI advancements (Alexander et al., 2019, p. 27 & Brooks, 2018 ). In correlation to the provided low-educational examples, learner’s engagement level has sparked the need for further exploration or development to validate AI in higher education (McMurtrie, 2018). 

In a poll by Gallup and Northeastern University, seventy-three percent of Americans believe that AI, also referred to as “Robot,” will replace the need for human contributions (Gimbel, 2018). Machine-based learning can personalize instruction using user-influenced learning algorithms and reverse-engineering processes to gain an expert level of understanding through the lens of the learner (Gimbel, 2018). In 2017 the MIT Sloan Management Review found 85% industry professionals agree that AI shows promising competitive advantages to their nitch, however only 20% have shown any indications for planning for AI integration (Alexander et al., 2019, p. 27). Fear of privacy, negativity, and bias algorithms influenced by the learner solidify the argument; AI should be used as a support tool for instructors (Means, 2009)

AI has demonstrated advanced approaches to feedback based on data analytics, synthesizing unique information processing, and minimizing time concentrating for real-time collaboration (Alexander et al., 2019, p. 27 & Gimbel, 2018). Chatbots are a form of AI widely accepted in learning cultures that have aided in instruction facilitation. In both educational and corporate learning environments, they have a positive influence on the instructor’s ability to challenge learner’s current interpretation, generating a personal learning experience. The learning experience is driven by sorting, assigning, and evaluating the information presented (McMurtrie, 2018). The storage of the AI at an introductory level uses machine-based logic to clone cognitive psychology to sort, organize, and recall information when presented with a stimulus (McMurtrie, 2018).

An illustration of baseline AI integration was demonstrated by Craig Coates, an entomologist at Texas A&M University. Coates faced challenges to facilitate a science course plagued by cheating (McMurtrie, 2018). In this example, Coates used AI to automate the plagiarism check, comparing text strings in a given database of knowledge. This allowed more time for: Coates to inspire an expert level of understanding and learners to process information at their own pace (McMurtrie, 2018). In this example, AI or robot is not the method of instruction but a useful tool when added in an instructional setting. In this context, technology does not demonstrate the driving characteristics to formulate privacy and ethical concerns (McMurtrie, 2018). Instructors use face-to-face interaction or social presence to deliver guidance that is contingent on the learner’s self-efficacy and task value matter (Artino, 2008). In summary, AI effectiveness relies on environmental integration and engagement levels through both the student and instructor lens. 

Facilitating instruction derived from learning theories, strategies, and styles in a blended environment use a combination tailored to the educational content. AI integration focuses on behavioralism, cognitive-constructivism, and connectivism. Behavioralism, as a stimulus-response theory, correlates directly with the AI, as a machine-based learning tool. Through connections, including peers, learning evolves to cognitive constructivism, where learners are considered self-efficient, but open to “construct” their desired understanding level. 


Alexander, B., Ashford-Rowe, K., Barajas-Murphy, N., Dobbin, G., Knott, J., McCormack, M., Pomerantz, J., Seilhamer, R., & Weber, N. (2019). Horizon Report 2019 Higher Education Edition. EDUCAUSE.

Artino Jr. AR. Promoting Academic Motivation and Self-Regulation: Practical Guidelines for Online Instructors. TechTrends: Linking Research & Practice to Improve Learning. 2008;52(3):37-45. doi:10.1007/s11528-008-0153-x.

Brooks, C. (2018, November 2). How artificial intelligence and virtual reality are changing higher ed instruction. Education Dive.

Gimbel, E. (2018, August 16). Artificial intelligence is poised to expand in higher education. Technology Solutions That Drive Education.

Means, Barbara, 1949-. (2009). Evaluation of evidence-based practices in online learning: A meta-analysis and review of online learning studies. Washington, DC: U.S. Dept. of Education, Office of Planning, Evaluation and Policy Development, Policy and Program Studies Service. Retrieved from

McMurtrie, B. (2018). How Artificial Intelligence Is Changing Teaching. The Chronicle of Higher Education40, 14.