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Cited by 3Artificial Intelligence in EducationArtificial Intelligence in Healthcare and EducationEducational Innovations and Challenges

Transforming U.S. higher education with artificial intelligence: Opportunities, ethical challenges, and strategies for effective implementation

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Kingsley Anyaso, Stanley Anyaso·2025·International Journal of Science and Research Archive
DOI 10.30574/ijsra.2025.14.3.2290
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Abstract

The US higher education system, renowned for its prestigious institutions and innovative research, faces significant challenges in accessibility, affordability, and equity. This paper explores the transformative potential of artificial intelligence (AI) and analytics in addressing these challenges. By leveraging AI-powered tools and big data analytics, higher education institutions can enhance personalized learning, improve student access and retention, streamline administrative processes, and support data-driven decision-making. However, the integration of AI also presents ethical considerations, including privacy concerns, potential biases in algorithms, and the risk of over-reliance on automated systems. This review examines the current landscape of AI in higher education, identifies key benefits and challenges, and proposes strategies for effective and ethical implementation. The findings suggest that while AI holds great promise for revolutionizing higher education, careful consideration and ongoing research are necessary to fully realize its potential and address associated risks.

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  • AI systems trained on historical data can perpetuate and amplify existing biases related to race, gender, socioeconomic status, and other demographic factors.

    “AI systems trained on historical data can perpetuate and amplify existing biases related to race, gender, socioeconomic status, and other demographic factors (Anyaso and Anyaso, 2025).”

    From A Multi-Dimensional Decision Framework for Evaluating and Selecting AI-Enabled Instructional Technology in U.S. Higher Education (2025) · cites this paper as Anyaso and Anyaso, 2025

    Section 4.4, “Bias in AI Algorithms,” says that algorithms trained on data reflecting existing biases may perpetuate or amplify those biases and produce discriminatory outcomes for some students. The passage does not identify race, gender, socioeconomic status, or other specific demographic factors, and frames the concern in higher education. (LLM, conf=0.85)

    Experimental — accuracy not yet measured

  • Students lacking reliable internet access, up-to-date devices, or digital literacy skills necessary to effectively employ AI tools may be placed at a disadvantage as AI becomes part of course requirements.

    “Students lacking reliable internet access, up-to-date devices, or the digital literacy skills necessary to effectively employ AI tools may be placed at a disadvantage as AI becomes part of course requirements (Anyaso and Anyaso, 2025).”

    From A Multi-Dimensional Decision Framework for Evaluating and Selecting AI-Enabled Instructional Technology in U.S. Higher Education (2025) · cites this paper as Anyaso and Anyaso, 2025

    Section 3.2 (page unavailable) discusses how AI may improve access, including for underserved students, but does not address whether students without reliable internet, current devices, or digital literacy are disadvantaged when AI is required in courses. Searches for those barriers and for unequal access to AI tools found no passage making the claimed point. (LLM, conf=0.74)

    Experimental — accuracy not yet measured

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Authors
Anyaso, K., & Anyaso, S.
Affiliations
  • Saint Louis University (US)
  • Akwa Ibom State University (NG)
Corresponding author
Kingsley Anyaso
Venue
International Journal of Science and Research Archive
Publisher
GSC Online Press
Published
2025-03-30
Volume
14
Issue
3
Pages
545-554
ISSN
2582-8185 (ISSN-L)
DOI
10.30574/ijsra.2025.14.3.2290
OpenAlex
W4408484612
Cited by
2
References
0
Citations by year
2026: 1 · 2025: 1
FWCI
2.10
Type
journal article
Language
en
Open access
Licence
cc-by
Locations
International Journal of Science and Research Archive (publishedVersion, cc-by)
Topics
  • Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
  • Physical Sciences › Computer Science › Information Systems › Educational Innovations and Challenges
Keywords
artificial intelligence in higher education, big data analytics, personalized learning, student retention, student access, administrative automation, data-driven decision making, algorithmic bias, privacy concerns, AI ethics, educational equity

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