Artificial Intelligence in Higher Education: A Case Study of Faculty Teaching Methodologies at a Private University

Ellen Ramsey; George Antoniou; Matteo Peroni; Karima Lanfranco; Brent Muckridge; Raouf Ghattas; Philip L. Fazio; Wendy Wallberg; Saidi Porta; Mary Smith; Gary Solomon; Kristen Migliano; David G. Wolf

Artificial Intelligence in Higher Education: A Case Study of Faculty Teaching Methodologies at a Private University

^*Corresponding author for this work

Research output: Contribution to conference › Conference session

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Abstract

This research study examined the integration of artificial intelligence (AI) in higher education from the perspective of the faculty of a private university. It inquired into the impact of AI on pedagogical methods, administrative procedures, and ethical values. Qualitative case study methodology and in-depth semi-structured interviews were designed and conducted with faculty from four academic departments. Responses related to impressions, challenges, and opportunities for AI integration were gathered. The study findings from qualitative and quantitative data analysis indicated that AI is perceived to help improve educational outcomes with student-personalized learning pathways through streamlined administrative processes. The study revealed that participating faculty expressed positive perceptions toward AI-enabled personalization. However, the study results highlighted several key themes: shortcomings in technology infrastructure, professional educational training development needs, and ethical considerations. Faculty highlighted in almost unanimous agreement the importance of maintaining pedagogical autonomy and human-centered teaching methods, particularly regarding algorithmic bias and educational equity. The study found critical ethical considerations centered on data privacy, algorithmic fairness, and protocols for academic integrity. This study advances theoretical knowledge of AI integration within higher education by articulating a new framework for ethical implementation that brings to light a balance between technological progress and pedagogical integrity. In conclusion, this study provides evidence-based recommendations for institutional policy development and faculty professional development programs regarding responsible AI adoption. The findings of this study improved the building literature on educational technology integration while offering practical insight into institutions of higher education currently in this technological transition. Qualitative case study methodology was utilized to conduct in-depth semi-informal interviews with faculty across four academic departments.

Original language	American English
State	Published - May 2 2025
Event	International Conference on Advances in Technology, Education and Science (ICATES) - Presented virtually, Trabzon, Turkey Duration: May 1 2025 → May 4 2025 Conference number: 2nd https://www.arste.org/icates/2025/ (The organizing committee of ICASTE would like to invite those working in areas related to technology, education and science to submit full paper or abstract, which will be presented as either oral or poster at the conference. Submissions will be double blind peer reviewed by experts in the field, and accepted full paper after registration and presentation will be published in the conference proceedings.)

Conference

Conference	International Conference on Advances in Technology, Education and Science (ICATES)
Abbreviated title	ICATES
Country/Territory	Turkey
City	Trabzon
Period	5/1/25 → 5/4/25
Other	2nd International Conference on Advances in Technology, Education and Science (ICATES), organized by the International Society for Academic Research in Science, Technology, and Education (ARSTE). The conference will take place on May 1-4, 2025, in Trabzon, Turkiye. The ICATES is an annual conference organized regularly. The ICATES 2025 is the second conference organized by ARSTE. The conference will be held in person. However, there is also the option of online/virtual participation.
Internet address	https://www.arste.org/icates/2025/ (The organizing committee of ICASTE would like to invite those working in areas related to technology, education and science to submit full paper or abstract, which will be presented as either oral or poster at the conference. Submissions will be double blind peer reviewed by experts in the field, and accepted full paper after registration and presentation will be published in the conference proceedings.)

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 4 Quality Education
SDG 9 Industry, Innovation, and Infrastructure
SDG 16 Peace, Justice and Strong Institutions
SDG 17 Partnerships for the Goals

Keywords

learning adaptivity and effectiveness
artificial intelligence influences
teaching methodologies
impact of ai in education
qualitative and quantitative methods
ethics
privacy

Disciplines

Higher Education

Organization custom fields

International conference presentation/presentation abroad
Organization is International

Access to Document

Artificial Intelligence in Higher Education: A Case Study of Faculty Teaching Methodologies at a Private UniversityFinal published version, 8.19 MB

http://spiral.lynn.edu/facpubs/2075

Artificial Intelligence in Higher Education: A Case Study of Faculty Teaching Methodologies at a Private University
Ramsey, E., Antoniou, G., Peroni, M., Muckridge, B., Ghattas, R., Fazio, P. L., Wallberg, W., Porta, S., Solomon, G., Smith, M., Migliano, K., Lanfranco, K., Qian, L. & Wolf, D. G., Jun 30 2025, In: International Journal of Academic Studies in Science and Education (IJASSE). 3, 1, p. 1-30 30 p.
Research output: Contribution to journal › Article › peer-review

Open Access
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Cite this

Ramsey, E., Antoniou, G., Peroni, M., Lanfranco, K., Muckridge, B., Ghattas, R., Fazio, P. L., Wallberg, W., Porta, S., Smith, M., Solomon, G., Migliano, K., & Wolf, D. G. (2025). Artificial Intelligence in Higher Education: A Case Study of Faculty Teaching Methodologies at a Private University. International Conference on Advances in Technology, Education and Science (ICATES), Trabzon, Turkey. http://spiral.lynn.edu/facpubs/2075

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title = "Artificial Intelligence in Higher Education: A Case Study of Faculty Teaching Methodologies at a Private University",

abstract = "This research study examined the integration of artificial intelligence (AI) in higher education from the perspective of the faculty of a private university. It inquired into the impact of AI on pedagogical methods, administrative procedures, and ethical values. Qualitative case study methodology and in-depth semi-structured interviews were designed and conducted with faculty from four academic departments. Responses related to impressions, challenges, and opportunities for AI integration were gathered. The study findings from qualitative and quantitative data analysis indicated that AI is perceived to help improve educational outcomes with student-personalized learning pathways through streamlined administrative processes. The study revealed that participating faculty expressed positive perceptions toward AI-enabled personalization. However, the study results highlighted several key themes: shortcomings in technology infrastructure, professional educational training development needs, and ethical considerations. Faculty highlighted in almost unanimous agreement the importance of maintaining pedagogical autonomy and human-centered teaching methods, particularly regarding algorithmic bias and educational equity. The study found critical ethical considerations centered on data privacy, algorithmic fairness, and protocols for academic integrity. This study advances theoretical knowledge of AI integration within higher education by articulating a new framework for ethical implementation that brings to light a balance between technological progress and pedagogical integrity. In conclusion, this study provides evidence-based recommendations for institutional policy development and faculty professional development programs regarding responsible AI adoption. The findings of this study improved the building literature on educational technology integration while offering practical insight into institutions of higher education currently in this technological transition. Qualitative case study methodology was utilized to conduct in-depth semi-informal interviews with faculty across four academic departments.",

keywords = "learning adaptivity and effectiveness, artificial intelligence influences, teaching methodologies, impact of ai in education, qualitative and quantitative methods, ethics, privacy",

author = "Ellen Ramsey and George Antoniou and Matteo Peroni and Karima Lanfranco and Brent Muckridge and Raouf Ghattas and Fazio, \{Philip L.\} and Wendy Wallberg and Saidi Porta and Mary Smith and Gary Solomon and Kristen Migliano and Wolf, \{David G.\}",

year = "2025",

month = may,

day = "2",

language = "American English",

note = "International Conference on Advances in Technology, Education and Science (ICATES), ICATES ; Conference date: 01-05-2025 Through 04-05-2025",

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Ramsey, E , Antoniou, G , Peroni, M, Lanfranco, K, Muckridge, B , Ghattas, R , Fazio, PL , Wallberg, W , Porta, S , Smith, M , Solomon, G , Migliano, K & Wolf, DG 2025, 'Artificial Intelligence in Higher Education: A Case Study of Faculty Teaching Methodologies at a Private University', International Conference on Advances in Technology, Education and Science (ICATES), Trabzon, Turkey, 5/1/25 - 5/4/25. <http://spiral.lynn.edu/facpubs/2075>

TY - CONF

T1 - Artificial Intelligence in Higher Education: A Case Study of Faculty Teaching Methodologies at a Private University

AU - Ramsey, Ellen

AU - Antoniou, George

AU - Peroni, Matteo

AU - Lanfranco, Karima

AU - Muckridge, Brent

AU - Ghattas, Raouf

AU - Fazio, Philip L.

AU - Wallberg, Wendy

AU - Porta, Saidi

AU - Smith, Mary

AU - Solomon, Gary

AU - Migliano, Kristen

AU - Wolf, David G.

N1 - Conference code: 2nd

PY - 2025/5/2

Y1 - 2025/5/2

N2 - This research study examined the integration of artificial intelligence (AI) in higher education from the perspective of the faculty of a private university. It inquired into the impact of AI on pedagogical methods, administrative procedures, and ethical values. Qualitative case study methodology and in-depth semi-structured interviews were designed and conducted with faculty from four academic departments. Responses related to impressions, challenges, and opportunities for AI integration were gathered. The study findings from qualitative and quantitative data analysis indicated that AI is perceived to help improve educational outcomes with student-personalized learning pathways through streamlined administrative processes. The study revealed that participating faculty expressed positive perceptions toward AI-enabled personalization. However, the study results highlighted several key themes: shortcomings in technology infrastructure, professional educational training development needs, and ethical considerations. Faculty highlighted in almost unanimous agreement the importance of maintaining pedagogical autonomy and human-centered teaching methods, particularly regarding algorithmic bias and educational equity. The study found critical ethical considerations centered on data privacy, algorithmic fairness, and protocols for academic integrity. This study advances theoretical knowledge of AI integration within higher education by articulating a new framework for ethical implementation that brings to light a balance between technological progress and pedagogical integrity. In conclusion, this study provides evidence-based recommendations for institutional policy development and faculty professional development programs regarding responsible AI adoption. The findings of this study improved the building literature on educational technology integration while offering practical insight into institutions of higher education currently in this technological transition. Qualitative case study methodology was utilized to conduct in-depth semi-informal interviews with faculty across four academic departments.

AB - This research study examined the integration of artificial intelligence (AI) in higher education from the perspective of the faculty of a private university. It inquired into the impact of AI on pedagogical methods, administrative procedures, and ethical values. Qualitative case study methodology and in-depth semi-structured interviews were designed and conducted with faculty from four academic departments. Responses related to impressions, challenges, and opportunities for AI integration were gathered. The study findings from qualitative and quantitative data analysis indicated that AI is perceived to help improve educational outcomes with student-personalized learning pathways through streamlined administrative processes. The study revealed that participating faculty expressed positive perceptions toward AI-enabled personalization. However, the study results highlighted several key themes: shortcomings in technology infrastructure, professional educational training development needs, and ethical considerations. Faculty highlighted in almost unanimous agreement the importance of maintaining pedagogical autonomy and human-centered teaching methods, particularly regarding algorithmic bias and educational equity. The study found critical ethical considerations centered on data privacy, algorithmic fairness, and protocols for academic integrity. This study advances theoretical knowledge of AI integration within higher education by articulating a new framework for ethical implementation that brings to light a balance between technological progress and pedagogical integrity. In conclusion, this study provides evidence-based recommendations for institutional policy development and faculty professional development programs regarding responsible AI adoption. The findings of this study improved the building literature on educational technology integration while offering practical insight into institutions of higher education currently in this technological transition. Qualitative case study methodology was utilized to conduct in-depth semi-informal interviews with faculty across four academic departments.

KW - learning adaptivity and effectiveness

KW - artificial intelligence influences

KW - teaching methodologies

KW - impact of ai in education

KW - qualitative and quantitative methods

KW - ethics

KW - privacy

M3 - Conference session

T2 - International Conference on Advances in Technology, Education and Science (ICATES)

Y2 - 1 May 2025 through 4 May 2025

ER -