Growing with Generative Artificial Intelligence: How Learning and Student Motivation Theory Can Inform Effective Pedagogies and Mitigate Academic Misconduct
Type of Presentation
Individual paper/presentation
Conference Strand
Ethics in Information
Target Audience
Higher Education
Second Target Audience
K-12
Relevance
The proposal brings together three elements, learning theories, theories of student motivation and readiness for learning, and existing research on student cheating and plagiarism behaviors. Considerations of these concepts can support information literacy educators in adapting their pedagogy to motivate students and mitigate the downsides of generative artificial intelligence in the classroom.
Proposal
The rise of generative artificial intelligence tools (GenAI) within education has brought about a number of concerns for educators. In particular, fear that students will use GenAI tools to complete assessments wholesale and will no longer demonstrate their learning have come to the forefront of early discussions. In essence, educators believe student motivation for learning will be supplanted by the ubiquitous and unfettered use of GenAI tools.
In reality, student motivation for learning and theories of student learning are complex. While some students, especially at the undergraduate level, remain motivated by external factors including job readiness, parental or instructor expectations (Bandura, 1997), or perceived difficulty of learning tasks (Eccles & Wigfield, 2002). As individuals mature, they become more intrinsically motivated to learn and see value in directly applying their learning to discrete tasks or personal learning and development needs (Knowles, 1984). Undergraduate students are actively engaged in the transition from being adolescent, externally motivated learners, to mature, self-motivated learners. This understanding of learning theory already supports empirically-based approaches to addressing academic misconduct (Perry, 2010), but can further inform approaches to educating productively with GenAI.
This presentation brings together three elements, learning theories, theories of student motivation and readiness for learning, and existing research on student cheating and plagiarism behaviors. Through an examination of these collective concepts, the presenters will provide recommendations on how common instructional strategies to enhance autonomy, competence, and relatedness can support the transition to GenAI enabled learning that simultaneously supports student growth.
References
Bandura, A. (1997). Self-efficacy: The exercise of control. Worth Publishers.
Eccles, J. S., & Wigfield, A. (2002). Motivational beliefs, values, and goals. Annual review of psychology, 53(1), 109-132. https://doi.org/10.1146/annurev.psych.53.100901.135153
Knowles, M. (1984). The adult learner: A neglected species (3rd Ed.). Houston: Gulf Publishing.
Perry, B. (2010). Exploring academic misconduct: Some insights into student behaviour. Active Learning in Higher Education, 11(2), 97-108. https://doi.org/10.1177/1469787410365657
Presentation Description
This presentation brings together three elements, learning theories, theories of student motivation and readiness for learning, and existing research on student cheating and plagiarism behaviors. Through an examination of these collective concepts, the presenters will provide recommendations on how common instructional strategies to enhance autonomy, competence, and relatedness can support the transition to GenAI enabled learning that simultaneously supports student growth.
Keywords
Learning Theory, Student Motivation, Andragogy, Pedagogy, Academic Misconduct, Plagiarism
Publication Type and Release Option
Presentation (Open Access)
Recommended Citation
Weaver, Kari D. and Lewitzky, Rachael A., "Growing with Generative Artificial Intelligence: How Learning and Student Motivation Theory Can Inform Effective Pedagogies and Mitigate Academic Misconduct" (2024). Georgia International Conference on Information Literacy. 13.
https://digitalcommons.georgiasouthern.edu/gaintlit/2024/2024/13
Growing with Generative Artificial Intelligence: How Learning and Student Motivation Theory Can Inform Effective Pedagogies and Mitigate Academic Misconduct
The rise of generative artificial intelligence tools (GenAI) within education has brought about a number of concerns for educators. In particular, fear that students will use GenAI tools to complete assessments wholesale and will no longer demonstrate their learning have come to the forefront of early discussions. In essence, educators believe student motivation for learning will be supplanted by the ubiquitous and unfettered use of GenAI tools.
In reality, student motivation for learning and theories of student learning are complex. While some students, especially at the undergraduate level, remain motivated by external factors including job readiness, parental or instructor expectations (Bandura, 1997), or perceived difficulty of learning tasks (Eccles & Wigfield, 2002). As individuals mature, they become more intrinsically motivated to learn and see value in directly applying their learning to discrete tasks or personal learning and development needs (Knowles, 1984). Undergraduate students are actively engaged in the transition from being adolescent, externally motivated learners, to mature, self-motivated learners. This understanding of learning theory already supports empirically-based approaches to addressing academic misconduct (Perry, 2010), but can further inform approaches to educating productively with GenAI.
This presentation brings together three elements, learning theories, theories of student motivation and readiness for learning, and existing research on student cheating and plagiarism behaviors. Through an examination of these collective concepts, the presenters will provide recommendations on how common instructional strategies to enhance autonomy, competence, and relatedness can support the transition to GenAI enabled learning that simultaneously supports student growth.
References
Bandura, A. (1997). Self-efficacy: The exercise of control. Worth Publishers.
Eccles, J. S., & Wigfield, A. (2002). Motivational beliefs, values, and goals. Annual review of psychology, 53(1), 109-132. https://doi.org/10.1146/annurev.psych.53.100901.135153
Knowles, M. (1984). The adult learner: A neglected species (3rd Ed.). Houston: Gulf Publishing.
Perry, B. (2010). Exploring academic misconduct: Some insights into student behaviour. Active Learning in Higher Education, 11(2), 97-108. https://doi.org/10.1177/1469787410365657
