artificial intelligence – technology driven pedagogy

by Food for ThoughtInstructional DesignLearning Theories

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. 

References

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. https://library.educause.edu/-/media/files/library/2019/4/2019horizonreport.pdf?la=en&hash=C8E8D444AF372E705FA1BF9D4FF0DD4CC6F0FDD1

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. https://www.educationdive.com/news/how-artificial-intelligence-and-virtual-reality-are-changing-higher-ed-inst/541247/

Gimbel, E. (2018, August 16). Artificial intelligence is poised to expand in higher education. Technology Solutions That Drive Education. https://edtechmagazine.com/higher/article/2018/08/artificial-intelligence-poised-expand-higher-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 http://eric.ed.gov/?id=eD505824

McMurtrie, B. (2018). How Artificial Intelligence Is Changing Teaching. The Chronicle of Higher Education, 40, 14.

Designs that Unclog Working Memory

by Brain-Based EducationFood for ThoughtInformation DesignInstructional DesignLearning TheoriesNeurosciencePsychology

The Organ

The human brain has an unlimited capacity for evolving knowledge. In instructional design, learners must be the center of all stages of a specific module via research, development, design, or implementation. How humans understand and process information through brain-based behavior can help deliver knowledge in ways that learners can receive, process, and store information adequately for later retrieval. While there is no direct link between neuroscience and how the brain processes information, there is excellent scientific evidence that the link has yet to be discovered (Jensen, 2008). Therefore, as facilitators of learning, it is crucial to understand how the brain, as an organ, functions (neuroscience) concerning education.

Inside the Cortex is where information processed is categorized into somatosensory (Parental lobes), visual (Occipital lobes), complex auditory (Temporal lobes), and lastly, “human” activities ( frontal lobes) (Ormrod, Schunk, & Gredler, 2009). After the Cortex’s lobes receive the information, knowledge remains in working memory until it is organized and stored for another similar stimulus. In summary, all knowledge is processed through the brain. How the brain uses perception, and relatability to organize and retrieve information can be classified as cognitive psychology backed by neuroscience. There is no direct relation between the two; however, one can’t exist without the other.

Information Overload

As outlined, the Cortex inside the brain is responsible for triggering responses to presented by stimuli, which can be presented in various fashions to the sensory receptors. Instructional designers can use neuroscience and how the brain interprets information through the effective use of sensory. Designing training plans should not over stimulate the sensory receptors in the CNS. Overwhelming the brain with the stimulus is no different than overworking your liver by consuming alcohol. Knowledge can be received and used while given a specific task, but with the more stimulus responses triggered, the less is committed organized long term memory. 

Cerbin defines working memory as the mental space where we do conscious, progressive thinking; however, that space has limited capacity (Cerbin, n.d.). This temporary storage allows cognitive information processing to manipulate storage later (Gutierrez, 2014). Think of working memory as a bucket; when full, the information is tossed or spilled. Even though the plastic material the bucket, made of is thin plastic, the design indentations of the bucket still lower the storage capacity. The working memory, “bucket,” uses part of the storage with tasks processed in an automatic method. When working memory is full, and the learner is challenged with many things to organize, overload sets in often resulting in a disengaged learner, but more importantly, the inability to recall responses.

Karla Gutierrez, SH!FT Disruptive eLearning contributor outlines how to design eLearning using working memory strategies, activities, and resources that will enhance cognitive processing skills using brain-function while not overstimulating. Working memory strategies help achieve a schema, receiving parts of the objective in smaller pieces (Ormrod, Schunk, & Gredler, 2009). To manage the information at each level of the pedagogy, activities, and resources help learners store information in an organization to easily retrieve under relatable circumstances. 

Conclusion 

All learning starts with the neuroscience of the brain. These discoveries have helped us understand how the brain receives processes and stores information. Where the brain stores, the data is contengient on the amount of working memory in use. To ensure learning is as simplistic for learners to process, instructional designers can use SH!FT’s suggested working memory strategies, activities, and resources. 

Resources

Cerbin, B. (n.d.). Working Memory as a Bottleneck in Learning – Exploring How Students Learn. Retrieved May 19, 2020, from https://sites.google.com/a/uwlax.edu/exploring-how-students-learn/working-memory-as-a-bottleneck-in-learning

Gutierrez, K. (2014, July 22). Designing eLearning to Maximize the Working Memory. Retrieved May 19, 2020, from https://www.shiftelearning.com/blog/bid/351491/Designing-eLearning-to-Maximize-the-Working-Memory

Jensen, Eric P. (2008). A Fresh Look at Brain-Based Education. Phi Delta Kappan, 89(6), 408–410. Retrieved from https://www.teachers.net/gazette/OCT08/jensen/

Ormrod, J., Schunk, D., & Gredler, M. (2009). Learning theories and instruction (Laureate custom edition). New York, NY: Pearson.