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From Traditional to Tech-Driven: The Role of AI in Shaping Student
Engagement and Performance in Accounting Education
Marzlin Marzuki*., Siti Sakinah Azizan
.
, Noora’in Omar.
,
Roshidah Safeei.
,
Nurul Fatihah Ilias
Faculty of Accountancy, Universiti Teknologi MARA, Kedah Branch, Sungai Petani Campus, Malaysia
*Corresponding Author
DOI: https://dx.doi.org/10.47772/IJRISS.2025.910000775
Received: 07 November 2025; Accepted: 14 November 2025; Published: 24 November 2025
ABSTRACT
The integration of Artificial Intelligence (AI) into accounting education is increasingly reshaping traditional
pedagogical approaches, offering innovative tools that enhance student engagement and academic
performance. Despite the growing availability of AI-driven technologies, many institutions continue to rely on
conventional teaching methods, limiting the potential for personalized and interactive learning experiences.
This study aims to explore how AI integration influences pedagogical innovation and ultimately improves
learning outcomes in accounting education. Using a narrative review methodology, relevant literature was
synthesized from peer-reviewed articles indexed in Scopus, focusing on AI applications in accounting, finance,
and higher education. The proposed conceptual framework integrates the Technology Acceptance Model
(TAM) and the AI Competency Adaptation Framework (AICAF) to explain how educators’ perceptions of
AI’s usefulness and ease of use influence instructional innovation. Findings reveal that AI-powered tools such
as intelligent tutoring systems, adaptive assessments, and real-time feedback mechanisms significantly
improve personalization, interactivity, and alignment of curricula with industry demands. However, effective
implementation is influenced by moderating factors, including faculty readiness and institutional support. This
study contributes to both academic discourse and practical application by proposing a structured model that
guides educators and policymakers in the strategic adoption of AI-based pedagogies. It also highlights the need
for systemic reforms in teacher training and infrastructure development to ensure equitable and sustainable AI
integration in accounting education.
Keywords: Artificial Intelligence (AI), Accounting Education, Pedagogical Innovation, Student Engagement,
Learning Outcomes
INTRODUCTION
The rapid evolution of digital technologies has fundamentally reshaped educational paradigms, with Artificial
Intelligence (AI) emerging as a pivotal force in transforming traditional learning environments into dynamic,
tech-driven ecosystems. As a discipline historically characterized by structured curricula and standardized
assessments, accounting education stands to benefit significantly from AI integration, which can drive
transformative pedagogical change. AI-powered tools such as intelligent tutoring systems, adaptive learning
platforms, and automated feedback mechanisms are redefining how students engage with complex accounting
concepts and develop critical competencies (Alsharari & Habashi, 2025). As the accounting profession evolves
toward data-driven decision-making and automation, equipping future accountants with both technical
expertise and technological fluency become imperative.
Research highlights the growing influence of AI in enhancing student engagement and academic performance
through personalized learning experiences. Radif (2024) emphasizes that AI facilitates tailored instruction by
adapting content delivery to individual learner profiles, thereby increasing motivation and comprehension.
Similarly, Mupaikwa (2025) underscores AI’s role in fostering interactive and immersive learning
environments that align with modern pedagogical goals. Furthermore, studies indicate that AI-enhanced
assessments provide real-time feedback, enabling learners to identify knowledge gaps and improve their
INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN SOCIAL SCIENCE (IJRISS)
ISSN No. 2454-6186 | DOI: 10.47772/IJRISS | Volume IX Issue X October 2025
www.rsisinternational.org
Page 9474
performance more effectively (Maree et al., 2024). Despite these demonstrated benefits, there remains a need
to synthesize these insights into a coherent and context-specific framework for accounting education.
However, the transition from traditional to AI-integrated teaching practices is not without challenges. Many
educators face barriers such as limited digital literacy, resistance to change, and insufficient institutional
support, which hinder the effective adoption of AI tools (Babo et al., 2024; Koralage et al., 2024). Moreover,
while AI offers potential for enhanced collaboration and peer learning, its implementation often lacks
structured guidance on how best to integrate these tools within existing curricula. These challenges limit the
widespread and impactful use of AI in accounting classrooms, constraining its potential to fully enhance
student engagement and learning outcomes.
To address this gap, this study proposes a conceptual framework that outlines the relationships between AI
integration, pedagogical innovation, and academic performance in accounting education. Drawing upon the
Technology Acceptance Model (TAM) and the AI Competency Adaptation Framework (AICAF), we aim to
establish a theoretical foundation that supports educators and institutions in implementing AI-based
pedagogical strategies. This paper contributes to the growing discourse on technology-enabled learning by
offering a structured model that guides curriculum development, instructional design, and policy formulation
in accounting education.
The significance of this research lies in its potential to inform stakeholders such as educators, administrators,
and policymakers about the strategic use of AI in enhancing educational outcomes. By addressing current
limitations and proposing a practical framework, this work encourages the adoption of AI tools that promote
inclusivity, personalization, and skill acquisition aligned with industry demands. The remainder of the paper is
organized as follows: Section 2 reviews the theoretical foundations, Section 3 presents the proposed conceptual
framework, Section 4 discusses implications and recommendations, and Section 5 provides concluding insights
and directions for future research.
LITERATURE REVIEW
A. AI Integration
The integration of Artificial Intelligence (AI) into accounting education has become increasingly prominent as
institutions seek to modernize teaching practices and align curricula with evolving industry demands.
Emerging technologies such as intelligent tutoring systems, adaptive testing platforms, and learning analytics
tools are being adopted to deliver personalized and interactive learning experiences (Karmakar & Das, 2024).
Tools like ChatGPT and Canva are also widely used in accounting classrooms, primarily for creating
instructional materials and supporting academic writing (Fachrurrozie et al., 2025). This shift toward AI-
enhanced pedagogy reflects broader trends in digital transformation across higher education, where automation
and data-driven decision-making are becoming central to effective teaching and learning. However, despite its
growing adoption, successful implementation remains hindered by practical barriers such as faculty readiness
and limited institutional infrastructure (Rizvi, 2023).
B. Pedagogical Innovation
AI is driving substantial pedagogical innovation in accounting education by enabling new forms of instruction,
assessment, and learner interaction. Intelligent tutoring systems and adaptive learning platforms allow
educators to customize content delivery based on individual student profiles, thereby fostering more inclusive
and engaging classroom environments (Radif, 2024). These tools support collaborative learning through peer
feedback mechanisms and by matching students with complementary learning styles (Alsharari & Habashi,
2025). Furthermore, AI-based simulations and virtual case studies offer experiential learning opportunities that
mirror real-world accounting scenarios, effectively bridging the gap between theoretical knowledge and
professional practice (Maree et al., 2024). While these advancements enhance instructional efficiency and
promote active learning, their full potential can only be realized when supported by structured frameworks that
guide educators in integrating AI within traditional pedagogical models.
INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN SOCIAL SCIENCE (IJRISS)
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C. Learning Outcomes
Research indicates that AI significantly enhances learning outcomes in accounting education by improving
student engagement, motivation, and academic performance. AI-based learning experiences have been shown
to positively influence students’ self-efficacy, career commitment, and professional readiness through the
development of AI literacy and digital competencies (Maulana et al., 2025). Personalized learning paths and
real-time feedback mechanisms enable learners to identify knowledge gaps, track progress, and improve
mastery of complex accounting concepts (Karmakar & Das, 2024). Automated grading and adaptive
assessments further contribute to a responsive learning environment, providing timely and targeted support
(Maree et al., 2024). Nevertheless, disparities in access to technology and the risk of overreliance on
automated systems may impact equity and critical thinking development (Rizvi, 2023). Therefore, balancing
technological integration with human-centered pedagogy is essential to maximize the educational benefits of
AI and prepare future accountants for a digitally transformed profession.
METHODOLOGY
A. Research Design – Narrative Review Methodology
This study adopts a narrative review methodology, which is particularly suitable for synthesizing existing
literature on emerging topics where conceptual clarity and theoretical development are needed (Green et al.,
2023). Unlike systematic reviews that focus on quantitatively aggregating empirical evidence, narrative
reviews allow for a more interpretive and thematic synthesis of diverse scholarly perspectives. This approach
supports the study’s aim of establishing a theoretical foundation that links AI integration, pedagogical
innovation, and learning outcomes in accounting education. By analyzing conceptual and empirical studies,
this method enables the identification of key themes, trends, and gaps in the literature, offering insights that
inform both academic discourse and practical implementation strategies.
B. Key Steps in Conducting a Narrative Review
The narrative review was conducted through a series of structured steps to ensure rigor and relevance. First, a
comprehensive search strategy was developed to identify peer-reviewed publications related to AI in
accounting and finance education. The search was limited to articles indexed in the Scopus database due to its
broad coverage of multidisciplinary research and high indexing standards. After an initial screening based on
titles and abstracts, full-text documents were reviewed to assess their alignment with the study’s objectives.
Articles were included if they addressed AI technologies, pedagogical practices, curriculum design, or learning
outcomes within accounting or finance-related educational contexts. Inclusion criteria prioritized recent
publications (2018–2025) to reflect current developments, while seminal works with foundational relevance
were retained regardless of publication date (Tranfield et al., 2022). Figure 1 illustrates the key stages of the
review process.
Fig.1 Key Steps in Conducting Narrative Review
INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN SOCIAL SCIENCE (IJRISS)
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C. Data Collection and Review Strategy
Data collection was executed using a structured Boolean search string applied to the Scopus database:
("artificial intelligence" OR "ai" OR "machine learning" OR "deep learning") AND ("accounting" OR
"finance" OR "bookkeeping" OR "audit") AND ("education" OR "learning" OR "training" OR "instruction")
AND ("curriculum" OR "pedagogy" OR "assessment" OR "evaluation") AND ("technology" OR "tools" OR
"software" OR "applications")
This multi-concept search ensured broad coverage across AI applications in financial disciplines and
pedagogical innovation. A total of 62 peer-reviewed articles were identified after applying inclusion and
exclusion criteria. An integrative thematic analysis approach was employed to analyze the selected literature.
This involved coding the content to identify recurring themes, concepts, and theoretical perspectives related to
AI adoption in accounting education. The identified themes were synthesized into a coherent narrative
structure that informed the development of the conceptual framework presented later in the paper (Braun &
Clarke, 2022).
D. Key Findings from the Narrative Review
The narrative review revealed several key findings, summarized in Table 1 below. These findings highlight
how AI is reshaping teaching and learning in accounting education, emphasizing the central role of
pedagogical innovation as a mediator between technology use and educational outcomes.
Collectively, these findings demonstrate that AI significantly influences accounting education by enabling
personalized instruction, streamlining assessment, and fostering pedagogical innovation. However, despite its
transformative potential, effective adoption remains contingent upon overcoming practical implementation
challenges. The integration of AI into curricula not only improves learning outcomes but also prepares students
for evolving industry demands, underscoring the need for institutional support and faculty training. These
insights collectively reinforce the necessity of developing a robust conceptual framework to guide educators
and policymakers in leveraging AI effectively within accounting pedagogy.
Table 1 Findings from the Narrative Review
Key Finding
Description
AI Enhances Personalized
Learning
AI tools such as intelligent tutoring systems and adaptive learning
platforms provide tailored educational experiences, improving student
engagement and comprehension (Radif, 2024; Karmakar & Das, 2024).
Pedagogical Innovation Through
AI Tools
Technologies like ChatGPT and Canva support content creation and
writing, enabling innovative teaching methods and interactive classroom
environments (Fachrurrozie et al., 2025).
Improved Assessment and
Feedback Mechanisms
AI automates grading and provides real-time feedback, helping learners
track progress and improve performance (Maree et al., 2024).
Development of AI Literacy and
Competencies
Exposure to AI-based learning enhances students’ digital competencies,
self-efficacy, and career readiness (Maulana et al., 2025).
Implementation Challenges in
AI Adoption
Barriers such as faculty readiness, technical preparedness, and
institutional infrastructure significantly affect the successful integration of
AI in accounting education (Rizvi, 2023).
Theoretical Framework
The theoretical foundation of this study is anchored in the Technology Acceptance Model (TAM) and the AI
Competency Adaptation Framework (AICAF), both of which provide valuable insights into how AI is
INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN SOCIAL SCIENCE (IJRISS)
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integrated into educational settings and its subsequent impact on pedagogical innovation and learning
outcomes (Davis, 1989; Arise & Moloi, 2025). TAM explains user acceptance of technology based on
perceived usefulness and ease of use which are factors that significantly influence educators’ willingness to
adopt AI tools in accounting instruction. Meanwhile, AICAF offers a structured approach to curriculum design
by identifying core competencies required for students to thrive in an AI-driven professional environment.
Together, these frameworks help contextualize the adoption and effectiveness of AI technologies in higher
education, particularly within accounting disciplines where digital transformation is reshaping professional
expectations.
Applying these theories to the context of accounting education, the proposed conceptual model integrates key
constructs such as AI integration, pedagogical innovation, and learning outcomes. Drawing from TAM, the
model assumes that when AI tools are perceived as useful and easy to use, they are more likely to be adopted
by instructors, thereby facilitating innovative teaching practices. These innovations enhance student
engagement through personalized learning experiences, real-time feedback, and interactive assessments (Radif,
2024; Karmakar & Das, 2024). The AICAF further enriches the framework by emphasizing the development
of AI literacy, critical thinking, and problem-solving skills, which are competencies essential for future
accountants navigating a technologically evolving profession (Maulana et al., 2025). By synthesizing these
perspectives, the framework provides a multidimensional lens to analyze how AI shapes educational processes
and outcomes in accounting education.
The practical implications of this integrative framework are significant for educators, institutional leaders, and
policymakers aiming to implement AI-based pedagogical strategies effectively. It offers a structured guide for
designing AI-integrated curricula, selecting appropriate tools, and assessing their impact on teaching and
learning. Furthermore, it underscores the need for faculty training and institutional support to overcome
barriers such as resistance to change and lack of technical expertise (Rizvi, 2023). In conclusion, this
framework not only contributes to academic discourse on AI in education but also provides actionable insights
for stakeholders seeking to align accounting education with the demands of Industry 4.0 and beyond.
Given the preceding discussions, Figure 2 illustrates the proposed conceptual framework of the study:
Fig. 2 Proposed Conceptual Framework
A. Proposition Development
1) AI Integration and Pedagogical Innovation
The integration of Artificial Intelligence (AI) into accounting education is increasingly recognized as a catalyst
for pedagogical innovation, transforming traditional instructional methods into dynamic, learner-centered
experiences. Technologies such as intelligent tutoring systems, adaptive learning platforms, and automated
feedback mechanisms enable educators to design personalized and interactive learning environments that
respond to individual student needs (Radif, 2024; Mupaikwa, 2025). These innovations enhance instructional
efficiency while fostering active learning, critical thinking, and problem-solving, which are competencies
essential for success in modern accounting practice. Furthermore, AI-driven tools support curriculum redesign
by embedding digital literacy and analytical reasoning into teaching practices, aligning pedagogical strategies
with evolving industry expectations (Arise & Moloi, 2025). The AI Competency Adaptation Framework
(AICAF) highlights that successful AI integration requires a rethinking of teaching methodologies to ensure
they promote both technical proficiency and higher-order cognitive skills (Maulana et al., 2025). As AI
reshapes how content is delivered, assessed, and experienced, it fundamentally transforms the educator’s role
from knowledge transmitter to facilitator of technology-enhanced learning. Given these developments, AI
INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN SOCIAL SCIENCE (IJRISS)
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plays a pivotal role in advancing pedagogical innovation within accounting education. Thus, this study
proposes the following proposition:
Proposition 1: AI Integration has a positive and significant effect on Pedagogical Innovation in accounting
education.
2) The Mediating Role of Pedagogical Innovation
Pedagogical Pedagogical Innovation serves as a critical mediator in translating AI integration into measurable
improvements in student learning outcomes. By leveraging AI-driven tools such as adaptive assessments,
intelligent feedback systems, and immersive simulations, educators can tailor instruction to individual learning
styles, thereby enhancing comprehension and retention (Alsharari & Habashi, 2025). These innovations enable
real-time performance tracking and personalized interventions, allowing learners to identify knowledge gaps
and improve at their own pace (Maree et al., 2024). Furthermore, AI-enabled collaborative platforms support
peer interaction and problem-based learning, which are key components in developing practical accounting
competencies (Koralage et al., 2024). As these pedagogical enhancements become embedded in teaching
practices, they directly influence key learning outcomes such as academic performance, engagement,
motivation, and career readiness (Radif, 2024; Maulana et al., 2025). Therefore, while AI provides the
technological foundation, it is through pedagogical innovation that its full educational impact is realized.
Based on this analysis, the following proposition is proposed:
Proposition 2: Pedagogical Innovation mediates the relationship between AI Integration and Learning
Outcomes in accounting education
3) The moderating role of Faculty Readiness and Institutional Support
The successful integration of AI into accounting education is not solely dependent on technological
capabilities but is also significantly influenced by organizational and contextual factors. Among these, Faculty
Readiness plays a critical moderating role, as educators must possess both the technical proficiency and
pedagogical understanding necessary to effectively utilize AI tools in instruction (Fachrurrozie et al., 2025).
Similarly, Institutional Support, including digital infrastructure, professional development programs, and
supportive policy frameworks, determines the extent to which AI can be adopted at scale across educational
settings (Rizvi, 2023). Without adequate institutional backing, even the most advanced AI tools may fail to
produce meaningful pedagogical outcomes. These moderating factors collectively shape the effectiveness of
AI integration and the resulting pedagogical innovation, ultimately influencing how learning outcomes are
achieved in AI-enhanced environments. Therefore, the following proposition is developed:
Proposition 3: Faculty Readiness and Institutional Support moderate the relationship between AI Integration
and Pedagogical Innovation in accounting education
Table 2 Summary of Propositions
Proposition
Statement
P1
AI Integration has a positive and significant effect on Pedagogical
Innovation in accounting education.
P2
Pedagogical Innovation mediates the relationship between AI Integration
and Learning Outcomes in accounting education.
P3
Faculty Readiness and Institutional Support moderate the relationship
between AI Integration and Pedagogical Innovation in accounting
education.
INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN SOCIAL SCIENCE (IJRISS)
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CONCLUSION AND IMPLICATIONS
This study highlights the transformative role of Artificial Intelligence (AI) in reshaping accounting education
by fostering pedagogical innovation and improving student learning outcomes. The integration of AI tools such
as intelligent tutoring systems, adaptive assessments, and real-time feedback mechanisms has demonstrated
significant potential in personalizing instruction, enhancing engagement, and supporting more effective
teaching practices. Drawing upon the Technology Acceptance Model (TAM) and the AI Competency
Adaptation Framework (AICAF), this paper proposes a conceptual framework that links AI integration,
pedagogical innovation, and learning outcomes, emphasizing the moderating roles of faculty readiness and
institutional support.
As a conceptual study relying on a narrative review methodology, it prioritizes thematic synthesis over
empirical validation. Consequently, the proposed framework has not yet been tested in real-world classroom
settings. The findings may not fully capture contextual challenges faced by institutions with limited
technological infrastructure or diverse student populations. Additionally, while moderating factors like faculty
readiness and institutional support are discussed, specific implementation models for different types of
institutions (e.g., rural vs. urban, public vs. private) were beyond the scope of this analysis.
Despite these limitations, the study offers practical value, particularly for under-resourced institutions seeking
to adopt AI incrementally. A phased adoption strategy is recommended, starting with low-cost, accessible tools
such as AI-powered writing assistants (e.g., ChatGPT) and open-access platforms. Professional development
programs should focus on building digital confidence among educators through peer mentoring, micro-
credentialing, and just-in-time training modules. Policymakers can further support equitable AI adoption by
establishing shared technology hubs or cloud-based solutions accessible across campuses. These steps can help
bridge the digital divide without requiring large-scale infrastructure investment.
Future research should empirically validate the proposed propositions across diverse educational contexts.
Studies could examine how socioeconomic status, regional infrastructure, and cultural attitudes toward
technology influence AI adoption in accounting classrooms. Moreover, longitudinal evaluations of
professional development programs aimed at reducing educator resistance to change are needed. Finally,
deeper exploration of ethical concerns, such as algorithmic bias, data privacy, and the risk of depersonalized
learning, is warranted to ensure responsible and inclusive use of AI in education.
ACKNOWLEDGEMENT
The authors would like to express their sincere gratitude to the Kedah State Research Committee, UiTM Kedah
Branch, for the generous funding provided under the Tabung Penyelidikan Am. This support was crucial in
facilitating the research and ensuring the successful publication of this article.
REFERENCES
1. Alsharari, N. M., & Habashi, J. I. (2025). Using AI to personalize learning in accounting education.
Studies in Computational Intelligence, 1208 , 479–499. https://doi.org/10.1007/978-3-031-89175-5_30
2. Arise, O. A., & Moloi, T. (2025). Towards a future-ready accounting education: Opportunities and
challenges of integrating artificial intelligence into contemporary accounting curricula. Springer
Proceedings in Business and Economics, 993–1015. https://doi.org/10.1007/978-3-031-84885-8_54
3. Babo, L., Mendonca, J. M. P., Queiros, R., Pinto, C. M. A., Cruz, M., & Mascarenhas, D. (2024).
Exploring HEIs students' perceptions of artificial intelligence on their learning process. In EEITE 2024
- Proceedings of 2024 5th International Conference in Electronic Engineering, Information Technology
and Education . IEEE. https://doi.org/10.1109/EEITE61750.2024.10654438
4. Braun, V., & Clarke, V. (2022). Thematic Analysis: A Practical Guide. Sage Publications.
5. Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information
technology. MIS Quarterly , 13(3), 319–340. https://doi.org/10.2307/249008
6. Fachrurrozie, F., Nurkhin, A., Santoso, J. T. B., & Wolor, C. W. (2025). Exploring the use of artificial
intelligence in Indonesian accounting classes. Cogent Education.
INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN SOCIAL SCIENCE (IJRISS)
ISSN No. 2454-6186 | DOI: 10.47772/IJRISS | Volume IX Issue X October 2025
www.rsisinternational.org
Page 9480
7. Green, S., Johnson, C., & Adams, D. (2023). Writing narrative literature reviews for applied research:
A structured approach. Journal of Applied Research in Higher Education, 15(2), 234–249.
https://doi.org/10.1108/JARHE-05-2022-0105
8. Karmakar, S., & Das, T. (2024). Effect of artificial intelligence on education. In Optimization and
Computing using Intelligent Data-Driven Approaches for Decision-Making: Optimization
Applications. https://doi.org/10.1007/978-3-031-42216-4_23
9. Koralage, M., Malagala, G., Wijemunige, N., De Silva, S., Banda, W., & Lokeshwara, A. (2024). From
chalkboards to chatbots: A systematic literature review on the evolution of AI in accounting pedagogy.
In 2024 6th International Conference on Advancements in Computing (ICAC) (pp. 61–66). IEEE.
https://doi.org/10.1109/ICAC64487.2024.10851039
10. Maree, M., Azmat, F., & Jameel, A. (2024). Role of AI in higher education: Opportunities and
challenges of using AI technologies. In SEFI 2024 - 52nd Annual Conference of the European Society
for Engineering, Proceedings: Educating Responsible Engineers (pp. 1840–1853). Zenodo.
https://doi.org/10.5281/zenodo.14256897
11. Maulana, A., Fenitra, R. M., Sutrisno, S., & Kurniawan. (2025). Artificial intelligence, job seeker, and
career trajectory: How AI-based learning experiences affect commitment of fresh graduates to be an
accountant? Computers and Education: Artificial Intelligence, 8, Article 100413.
https://doi.org/10.1016/j.caeai.2025.100413
12. Mupaikwa, E. (2025). Transforming higher education through technology: The impact of artificial
intelligence in Education 5.0. In Impacts of AI on Students and Teachers in Education 5.0 (pp. 553–
579). IGI Global. https://doi.org/10.4018/979-8-3693-8191-5.ch020
13. Radif, M. (2024). Artificial intelligence in education: Transforming learning environments and
enhancing student engagement. Educational Sciences: Theory and Practice, 24(1), 93–103.
https://doi.org/10.12738/jestp.2024.1.008
14. Rizvi, M. (2023). Exploring the landscape of artificial intelligence in education: Challenges and
opportunities. In HORA 2023 - 2023 5th International Congress on Human-Computer Interaction,
Optimization and Robotic Applications (Proceedings). IEEE.
https://doi.org/10.1109/HORA57465.2023.10157743
15. Tranfield, D., Denyer, D., & Smart, P. (2022). Towards a methodology for developing evidence-
informed management knowledge: How systematic review can aid theory building. British Journal of
Management, 33(1), 1–21. https://doi.org/10.1111/1467-8551.12531