THE IMPACT OF ARTIFICIAL INTELLIGENCE BASED LEARNING TOOLS ON STUDENTS PERFORMANCE AND ENGAGEMENT IN HIGHER EDUCATION
Abstract
The integration of Artificial Intelligence (AI) in
higher education has catalyzed a significant transformation in teaching,
learning, and assessment practices. This review examines empirical evidence on
the impact of AI-based learning tools such as intelligent tutoring systems,
adaptive learning platforms, chatbots, and predictive analytics on student
academic performance and engagement. Drawing from 39 peer-reviewed studies
published between 2017 and 2024, the review employs a systematic methodology
guided by PRISMA protocols to identify thematic patterns across diverse higher
education settings. The findings reveal that AI technologies significantly
enhance academic performance by offering personalized learning paths, real-time
feedback, and data-informed instructional support. Additionally, AI fosters
behavioral, cognitive, and emotional engagement by promoting interactive,
student-centered learning environments. Tools such as emotion-aware chatbots
and multimodal tutors contribute to increased motivation and learning
persistence. However, the review also highlights critical challenges including
algorithmic bias, data privacy concerns, institutional readiness, and ethical
governance. While many institutions have embraced AI-driven innovations,
disparities in infrastructure and digital literacy present barriers to
equitable implementation. The discussion aligns these findings with educational
theories such as Vygotsky’s Zone of Proximal Development and Deci and Ryan’s
Self-Determination Theory, emphasizing the pedagogical value of AI when
ethically and thoughtfully integrated. This review contributes to knowledge by
offering a nuanced synthesis of AI’s dual potential as both an enabler and
disruptor in higher education. It concludes with practical recommendations for
responsible AI adoption and identifies gaps for future research on scalability,
inclusion, and long-term outcomes.
Keywords:
DVFS
Energy aware
sleep mode
carbon footprint
trade-off
DOI:
https://doi.ms/10.00420/ms/5611/LNND2/ARQ
|
Volume: 1212
|
Issue: 12121
|
Pages: 1-8
|
Views: 0
Document Not Available
No document file has been uploaded for this article.