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Advances in Social Sciences Research Journal – Vol. 12, No. 1
Publication Date: January 25, 2025
DOI:10.14738/assrj.121.18121.
Zhang, J. (2025). Generative AI in Higher Education: Challenges and Opportunities for Course Learning. Advances in Social Sciences
Research Journal, 12(1). 11-18.
Services for Science and Education – United Kingdom
Generative AI in Higher Education: Challenges and Opportunities
for Course Learning1
Jinbao Zhang
Beijing Normal University, Beijing, China
ABSTRACT
Generative Artificial Intelligence (GenAI) is transforming course learning in higher
education. This study, based on two rounds of action research in two college
courses, reveals critical challenges in this transformation. Findings indicate that
students' reliance on GenAI can hinder their autonomy, critical thinking, and
affective-social learning, while also raising concerns about content accuracy,
intellectual property rights, and privacy. Although teacher guidance partially
mitigates these issues, enhanced strategies are crucial. This study provides
practical implications for optimizing curriculum design, teaching methods, and
evaluation criteria, emphasizing the need to incorporate ethics education and
strengthen emotional-social guidance. Future research directions are suggested to
address the challenges of GenAI and ensure educational quality and student
development.
Keywords: Generative Artificial Intelligence, Higher Education, Course Learning,
Challenges, Ethical Concerns, Teaching Strategies, Student Development.
INTRODUCTION
Generative AI is rapidly changing education, presenting both opportunities and challenges
(Tariq, 2024; Batta, 2024). Its ability to generate content has significant implications for
teaching and learning (Mittal, Sai, & Chamola, 2024), impacting code writing (Gottlander &
Khademi, 2023), humanities assignments (Kumar et al., 2023; Hutson, 2025), and personalized
feedback (George, 2023; Guettala et al., 2024).
However, concerns remain about its potential impact on student learning, ethics, and social
skills (Hoernig et al., 2024). While some research suggests AI can enhance learning (Hooda et
al., 2022), others caution against over-reliance (Aymen & Zakarya, 2024). Ethical
considerations, such as intellectual property and data privacy, are also paramount (Kong, Li, &
Zhang, 2024).
This study addresses existing research gaps by examining GenAI's application in two university
courses: "Computational Thinking and Social Sciences" and "Modern Educational Technology."
Using action research, the study analyzes AI's influence on learning, ethics, and social skills,
aiming to provide a comprehensive understanding of its role in higher education.
1 This research was funded by the Key Project of Beijing Social Science Fund in 2019, 'Research on Social Science Issues
of Artificial Intelligence and Its Educational Practice' (Grant No. 19JYA002).
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LITERATURE REVIEW
Generative AI (GAI) is rapidly gaining traction in education, particularly in college courses.
While it offers promising potential, it also presents challenges that necessitate careful
consideration.
Current Applications and Benefits
GAI is being used in higher education to enhance teaching and personalize learning (Guettala
et al., 2024). It can increase student engagement, improve test scores, and accelerate skill
development (Guettala et al., 2024). Additionally, GAI provides educators with innovative tools
and resources for course design, enriching the learning experience (Moundridou et al., 2024).
When used effectively, GAI can increase teacher productivity and enhance instructional
capabilities (MacDowell et al., 2024).
Challenges and Concerns
Despite its potential benefits, GAI presents several challenges:
• Technology Dependence: Over-reliance on GAI can hinder students' independent
thinking and problem-solving abilities (Le-Nguyen & Tran, 2024; Shah & Asad, 2024).
• Content Accuracy: GAI-generated content may contain misinformation or biases due to
limitations in training data and algorithms (Ozbay & Alatas, 2020).
• Ethical Issues: The use of GAI raises concerns about intellectual property infringement
and data privacy breaches (Shukla et al., 2022; Vaza et al., 2024).
• Social and Emotional Impact: Excessive GAI use may negatively affect students' social
interactions and teamwork skills (Wach et al., 2023).
Addressing the Challenges
Researchers propose several strategies to mitigate these challenges:
• Digital Literacy: Educators need to strengthen students' digital literacy skills to help
them critically evaluate and use GAI responsibly (Kazanidis & Pellas, 2024; Cha et al.,
2024).
• Critical Thinking: Students should be encouraged to think critically about GAI- generated content and verify information using reliable sources (Hammer, 2024;
Holmes et al., 2023).
• Ethical Guidelines: Educational institutions must establish clear ethical guidelines and
norms for GAI use to promote responsible and ethical practices (Holmes et al., 2023;
Kasneci et al., 2023).
This study aims to build upon existing research by investigating the practical applications of
GAI in college courses, its impact on student learning, and strategies for addressing ethical
concerns.
THEORETICAL FRAMEWORK
This study examines the impact of generative AI on higher education, focusing on technology
dependence, ethical issues, and assessment, through a multidisciplinary lens (Chiu, 2023). The
theoretical framework integrates:
• Educational Technology: The Technology Acceptance Model (TAM) (Davis, 1989) is
used to analyze factors influencing students' AI adoption. The study also examines
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Zhang, J. (2025). Generative AI in Higher Education: Challenges and Opportunities for Course Learning. Advances in Social Sciences Research Journal,
12(1). 11-18.
URL: http://dx.doi.org/10.14738/assrj.121.18121
technology dependence, echoing the concept of "AI literacy" (Zawacki-Richter et al.,
2023), where students must critically evaluate AI tools.
• Social Cognitive Theory (SCT): Bandura's (1997) theory of self-efficacy is used to
explore how AI affects students' confidence. The study also analyzes students' outcome
expectations related to AI use.
• Ethics: The study addresses academic integrity concerns, such as plagiarism and
cheating, raised by AI use (Yi, 2021). It also examines potential intellectual property
issues.
• Constructivist Learning Theory: Drawing on constructivism (Hein, 1991), the study
investigates how AI impacts students' knowledge construction process.
Based on this framework, the study proposes hypotheses regarding technology dependence,
academic integrity, assessment, and knowledge construction. It suggests that self-efficacy and
outcome expectations influence AI dependence, with potential implications for academic
integrity. AI use may also necessitate changes in assessment methods. Finally, the study
recognizes the dual impact of AI on knowledge construction, acknowledging its potential to
both promote and hinder deep learning.
The study aims to test these hypotheses through action research and provide recommendations
for the ethical and effective integration of generative AI in higher education.
RESEARCH DESIGN
This study employs an action research approach (Kemmis & McTaggart, 1988) to investigate
the impact of generative AI on college course learning. This method emphasizes a cyclical
process of planning, acting, observing, and reflecting, allowing for continuous improvement
throughout the research.
Focus Areas
The study focuses on several key areas:
• Technology Dependence: Examining whether students over-rely on generative AI,
potentially hindering their independent learning and critical thinking skills.
• Social Cognitive Impact: Analyzing how generative AI affects students' self-efficacy and
outcome expectations in the learning process.
• Ethical Considerations: Investigating potential ethical dilemmas related to academic
integrity and intellectual property rights when using generative AI.
• Assessment Impact: Exploring how generative AI influences teachers' ability to
accurately assess student learning and understanding.
Research Subjects
The study involves undergraduate students enrolled in two courses at a university:
"Computational Thinking and Social Sciences" (third-year students) and "Modern Educational
Technology" (first-year students). These courses were selected for their distinct student
populations, allowing for a comprehensive examination of how different student groups engage
with generative AI.
Rationale for Course Selection:
• "Computational Thinking and Social Sciences": Third-year students in this course may
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have a higher tendency to over-rely on AI tools due to their focus on problem-solving
and research.
• "Modern Educational Technology": First-year students in this course are likely to have
a more basic understanding of AI and its capabilities, potentially leading to misuse.
Research Process
The study was conducted in two rounds of action research, each involving problem
identification, subject selection, research design, implementation, and reflection and analysis.
Data collection methods included classroom observation, assignment analysis, and student
interviews.
ANALYSIS OF ISSUES
This section analyzes key issues observed in the study concerning students' use of generative
AI in the two courses.
Technology Dependence and Deviation from Learning Principles
Students in both courses exhibited a strong reliance on generative AI, confirming the
hypothesis that students tend to depend on such technology.1 They often sought answers
directly from AI instead of engaging in independent thinking and utilizing their knowledge base.
This over-reliance reflects a neglect of developing essential learning skills and aligns with the
hypothesis that students with low self-efficacy are more prone to AI dependence.
Variances:
• Degree and Scenarios: Third-year students in "Computational Thinking and Social
Sciences" showed greater dependence when applying professional knowledge,
reflecting their lower self-efficacy and desire for quick solutions to complex tasks. First- year students in "Modern Educational Technology" relied heavily on AI across all tasks,
indicating a lack of active learning habits and a desire for shortcuts.
• Impact Focus: Over-reliance on AI hindered the development of critical thinking and
problem-solving skills in third-year students, while first-year students struggled with
independent program design, highlighting the negative impact of AI dependence on
professional competence.
Content Accuracy and Ethical Concerns
Both courses encountered issues with the accuracy of AI-generated content and related ethical
concerns, linking to academic integrity considerations. Students often used inaccurate
information without verification, potentially leading to unintentional academic dishonesty.
Additionally, they demonstrated a weak awareness of ethical issues like intellectual property
and privacy protection, highlighting the risk of violating academic norms when using AI.
Variances:
• Course Content: In "Computational Thinking and Social Sciences," inaccurate
information and ethical oversights in assignments and projects revealed the potential
for AI to negatively impact research validity. In "Modern Educational Technology,"
issues included inaccurate information about emerging technologies and the use of
copyrighted materials, demonstrating that AI challenges manifest differently across
disciplines.
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• Teaching Methods: Provide practical guidance on using AI, incorporating case studies
and group discussions to enhance critical evaluation and social interaction.
• Assessment: Develop comprehensive evaluation criteria that consider the influence of
AI on student work, focusing on understanding and application of knowledge.
• Social-Emotional Learning: Encourage active learning and social interaction through
group activities and projects, fostering collaboration and emotional well-being.
• Ethical Integration: Embed ethical considerations throughout the curriculum,
emphasizing intellectual property, privacy, and responsible AI use.
Future Research Directions
Future research should focus on developing targeted interventions, such as educational toolkits
and modules, to cultivate critical thinking and digital ethics in students using generative AI.
Exploring technological solutions for monitoring and guiding AI use can also enhance
responsible application and improve assessment accuracy.
Further investigation into the disciplinary nuances of AI application is crucial. Examining how
different fields engage with AI can inform tailored pedagogical strategies and promote effective
integration across diverse learning contexts.
By addressing these challenges and pursuing further research, educators can harness the
potential of generative AI while mitigating its risks, ultimately fostering a more effective and
ethical learning environment for students.
References
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Forum of Teaching & Studies, 20(1).
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Critical Thinking (Doctoral dissertation, university center of abdalhafid boussouf-MILA).
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Intelligence (AI) and their Psychosocial Maturity. TWIST, 19(3), 339-344.
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