Empowering Education: The Role of AI in Supporting Student Success
By Karen Johnson, EdD
Contributors: Janice Terrell, EdD; Jennifer Doran, MA; Anne Moore, MFS
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Context
In the rapidly evolving landscape of higher education, artificial intelligence (AI) is emerging as a powerful tool to enhance student learning and academic success. As educators and institutions grapple with the potential and challenges of AI integration, a new qualitative exploratory case study is being conducted to shed light on faculty perspectives and practices in using AI, particularly Large Language Models (LLMs), to support graduate students. The growing need to understand how faculty can effectively leverage AI in educational contexts is evident. While technological advancements have opened up new possibilities, there remains a lack of comprehensive understanding of educators' optimal use of AI tools. This gap results in missed opportunities to support diverse student populations and prepare them for increasingly digital workplaces (Sullivan et al., 2023). Current AI research has identified numerous ways in which AI is being utilized in higher education, both by students and faculty. This study was conducted as part of Center for Educational and Instructional Technology Research Lab to help close the gap.
Educational Applications
These applications demonstrate the potential of AI to transform various aspects of the educational experience, from enhancing student skills to streamlining administrative tasks for faculty.
Student Uses:
1. Supporting the writing process (Cope et al., 2020)
2. Brainstorming and building research theories (Christou, 2023)
3. Providing immediate writing feedback (Schmohl et al., 2020)
4. Facilitating independent information processing and mastery (Chaudhry & Kazim, 2021)
Faculty Uses:
1. Summarizing learning outcome data and designing assessments (Chaudhry & Kazim, 2021)
2. Identifying and addressing student learning gaps (Koh et al., 2023)
3. Differentiating instruction methods to meet individual student needs.
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Challenges in AI Implementation
Despite the promising applications, integrating AI into higher education is not without its challenges. The existing literature reveals a divide between those who view AI as an exciting tool and those who fear its negative repercussions. Some of the key concerns include:
1. Student privacy rights (Nguyen, 2023)
2. Assignment accountability and integrity (Storey, 2023)
3. Potential detriment to students' critical-thinking skill development (Storey, 2023)
4. Risks of plagiarism and devaluation of academic degrees (Cotton et al., 2023)
These challenges underscore the need for careful consideration and ethical guidelines in implementing AI in educational settings. The qualitative exploratory case study addresses the gap in understanding and will provide empirical evidence on the uses of AI in higher education. The study aims to investigate the current practices, challenges, effectiveness, and opportunities for expansion of higher education faculty's use of LLM AI in providing academic support to graduate students.
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Research Questions
The following Research Questions guided our study.听
1. What are higher education faculty members' perceived challenges in utilizing LLM AI to provide support to students toward improving academic outcomes?
2. How effective do higher education faculty members perceive their uses of LLM AI to be in providing support to students toward improving academic outcomes?
3. What do higher education faculty members consider opportunities for improvement and effective expansion of the uses of LLM AI in supporting students and improving academic outcomes?
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Methodology
The study employs a purposive sampling approach to identify 12-15 faculty members who meet specific inclusion criteria, including current employment in higher education, experience with LLM AI, and affiliation with an institution that has established AI policies. Data collection involved semi-structured interviews conducted via video conferencing platforms and the analysis of institutional AI policy documents. The interviews explored participants' perceptions and experiences using AI to support student learning and academic success. Data analysis utilized a hybrid coding approach, combining deductive coding based on preexisting concepts from the research questions and literature review with inductive coding to identify new themes emerging from the raw data.
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Implications and Future Directions
This research has the potential to provide valuable insights into the current state of AI use in higher education, as well as identify best practices and areas for improvement. By exploring faculty perspectives on the challenges and opportunities associated with AI integration, the study may inform the development of more effective strategies for leveraging AI to support student learning outcomes. As AI continues to evolve and permeate various aspects of education, educators, and institutions must stay informed about its potential benefits and drawbacks. This study represents an important step in building a knowledge base to guide AI's responsible and effective integration in higher education settings. The findings from this research may also contribute to developing more comprehensive AI policies and guidelines for educational institutions, ensuring that the use of AI aligns with academic integrity standards and ethical considerations.
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Conclusion
The results of this study, anticipated for publication in early 2025, will provide valuable insights for educators, administrators, and policymakers seeking to shape the future of higher education in an increasingly AI-driven world. Currently, our research team is in the data collection and analysis phase. We anticipate that higher education faculty will reveal the crucial role they are playing in helping students leverage AI tools for tasks such as conducting literature reviews, refining research design and methodology, analyzing data, and enhancing writing skills. However, the findings may also reveal significant challenges, including ethical dilemmas arising from the widespread availability of AI and its use by students. Another likely finding is the need for institutional policies to keep pace with the rapid implementation of AI, ensuring that risks associated with its use are addressed through guidelines that promote transparency, accountability, and ethical research practices (Terrell, 2024).
As we stand on the brink of a new era in education, the integration of AI presents both exciting opportunities and significant challenges. By exploring faculty perspectives and experiences with AI, this study aims to contribute to a more nuanced understanding of how these powerful tools can be harnessed to support student success while navigating the complex ethical and practical considerations involved.听
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References
Chaudhry, M. & Kazim, E. (2021). Artificial intelligence in education (AIEd): a high-level academic and industry note 2021. AI and Ethics. 2. 1-9.
Christou, P. (2023). The use of artificial intelligence (AI) in qualitative research for theory development. The Qualitative Report, 28(9), 2739-2755.
Cope, B., Kalantzis, M. & Searsmith, D. (2020). Artificial intelligence for education: Knowledge and its assessment in AI-enabled learning ecologies. Educational Philosophy and Theory, 53, 1-17.
Cotton, D. R. E., Cotton, P. A., & Shipway, J. R. (2024). Chatting and cheating: Ensuring academic integrity in the era of ChatGPT. Innovations in Education and Teaching International, 61(2), 228鈥239.
Koh, J., Cowling, M., Jha, M., & Sim, K. N. (2023). The human teacher, the AI teacher and the AIed-teacher. Relationship. Journal of Higher Education Theory and Practice, 23(17).
Nguyen, A., Ngo, H.N., Hong, Y. et al. (2023). Ethical principles for artificial intelligence in education. Educ Inf Technol 28, 4221鈥4241.
Schmohl, T., Watanabe, A., Frolich, N., & Herzberg, D. (2020). How can artificial intelligence improve the academic writing of students? Electric Platform for Adult Learning in Europe, 10. 168-171.
Storey, V. A. (2023). AI technology and academic writing: Knowing and mastering the "craft skills.鈥 International Journal of Adult Education and Technology (IJAET), 14(1), 1-15.
Sullivan, M., Kelly, A., & McLaughlan, P. (2023). ChatGPT in higher education: Considerations for academic integrity and student learning. Journal of Applied Learning and Teaching, 6(1).
Terrell, J.D. (2024). Ethical considerations in the integration of artificial intelligence in doctoral dissertation research. Phoenix Scholar. 7(1). 47-50.
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Karen Johnson, Ed.D.
ABOUT THE AUTHOR
Karen Johnson, Ed.D., is a research methodology group leader in the University鈥檚 Center for Educational and Instructional Technology Research (CEITR). A faculty member at the 澳门天天彩开奖记录 since 2005, she currently serves as a University Research Methodologist for CDS. She is also a reviewer for CEITR鈥檚 dissertation to publication workshop and a second-tier reviewer for the international journal, The Qualitative Report. Johnson earned a doctorate in Higher Education from Texas Tech University and completed her Master of Arts and bachelor鈥檚 degrees from the University of Texas.