CURRENT RESEARCH JOURNAL OF PEDAGOGICS (ISSN: 2767-3278)
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VOLUME:
Vol.06 Issue03 2025
Page: - 01-04
RESEARCH ARTICLE
The Influence of AI Integration on Teaching Effectiveness:
Examining Teacher Adoption, Ease of Use, Experience, and
Student Interest
Karl Mayr
Department of Primary and Secondary Teacher Education, Oslo Metropolitan University, Oslo, Norway
Received:
03 January 2025
Accepted:
02 February 2025
Published:
01 March 2025
INTRODUCTION
The rapid advancement of Artificial Intelligence (AI)
technologies has transformed various industries, and
education is no exception. AI tools are increasingly being
adopted to enhance the teaching-learning process, offering
personalized
learning
experiences,
automating
administrative tasks, and providing insights into student
performance. However, despite the growing adoption of AI
in education, its effectiveness is contingent on several
factors, particularly the adoption and ease of use of AI tools
by teachers, as well as their experience in utilizing these
tools.
Moreover, the role of students' interest in learning cannot
be overlooked. Student interest plays a crucial role in
determining the success of educational interventions. This
study aims to explore the following key questions:
1.
How does the adoption of AI, the perceived ease of
use, and teachers’ experience with AI affect teaching
effectiveness?
2.
What role does student interest play in moderating
ABSTRACT
Introduction: The integration of Artificial Intelligence (AI) in education has gained prominence in recent years, promising to
revolutionize teaching and learning. However, the effectiveness of AI in education depends on factors such as teachers' adopt ion
of AI technologies, ease of use, and their level of experience with AI tools. Additionally, student interest in learning may moderate
the impact of AI on teaching effectiveness. This study explores how these factors—adoption, ease of use, and teacher
experience—affect teaching effectiveness and the moderating role of student interest.
Methods: A survey-based quantitative approach was employed, with data collected from 250 teachers and 400 students across
various educational institutions. Teachers were surveyed regarding their adoption of AI tools, perceived ease of use, and
experience with AI technologies. Students were asked to assess their interest levels in learning through AI-based tools. Teaching
effectiveness was measured using a combination of teacher self-assessment and student evaluations.
Results: The findings indicate that adoption and ease of use of AI tools positively correlate with teaching effectiveness.
Additionally, teachers with higher experience in using AI report better teaching outcomes. Student interest was found to
significantly moderate the relationship between AI adoption and teaching effectiveness, with higher levels of student interest
amplifying the positive impact of AI.
Discussion: The study underscores the importance of teacher preparedness and familiarity with AI in enhancing teaching
effectiveness. Moreover, the moderating role of student interest highlights the need to align AI-based learning tools with students'
preferences to maximize engagement and learning outcomes.
Keywords:
AI Integration in Education, Teaching Effectiveness, Teacher Adoption of AI, Ease of Use, Teacher Experience with AI, Student Interest in AI,
Educational Technology, AI in Classroom Learning, Digital Pedagogy, Adaptive Learning Systems.
CURRENT RESEARCH JOURNAL OF PEDAGOGICS (ISSN: 2767-3278)
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the relationship between AI integration and teaching
effectiveness?
This research seeks to fill the gap in literature by exploring
how these factors interplay to influence teaching outcomes
and to provide insights into how AI can be more effectively
incorporated into educational settings.
Literature Review
AI Adoption and Teaching Effectiveness
The adoption of AI in educational settings has been shown
to
enhance
teaching
effectiveness
by
providing
personalized learning experiences, improving content
delivery, and facilitating better assessment methods.
However, teachers’ willingness to adopt AI is influenced
by several factors, including perceived benefits, ease of
use, and organizational support (Venkatesh et al., 2003).
When teachers actively adopt AI technologies, they can
tailor instruction to meet diverse student needs, thus
improving teaching outcomes (Baker et al., 2019).
Ease of Use and Teaching Effectiveness
Ease of use is another significant factor in the effective
integration of AI in teaching. If AI tools are complex or
require extensive training, teachers may be reluctant to use
them, thereby diminishing their potential effectiveness.
The Technology Acceptance Model (TAM) suggests that
the perceived ease of use directly influences the likelihood
of technology adoption (Davis, 1989). Studies have shown
that teachers who find AI tools easy to use are more likely
to incorporate them into their teaching practices, resulting
in higher teaching effectiveness (Liu et al., 2019).
Teachers’ Experience with AI
Teachers’ experience with AI tools also plays a critical role
in the effectiveness of teaching. Teachers who have more
experience with AI are better equipped to use these
technologies to enhance their teaching strategies. AI allows
for adaptive learning, which can be particularly beneficial
for differentiated instruction. Experienced teachers are
more likely to use AI in a way that aligns with their
teaching goals, leading to better student outcomes (Ally,
2008).
Student Interest as a Moderator
Student interest in learning is a well-documented factor in
determining academic success. Interest in a subject
enhances students’ motivation to engage with the material,
leading to better learning outcomes (Schunk, Pintrich, &
Meece, 2008). In the context of AI, student interest can
amplify the effectiveness of AI tools. If students are
interested in using AI-based learning tools, they are more
likely to engage with the content and benefit from
personalized learning experiences. Thus, student interest
may moderate the relationship between AI adoption and
teaching effectiveness.
METHODS
Participants
The study involved two groups of participants: 250
teachers and 400 students from various schools and
universities that had integrated AI tools into their
educational practices. The teachers included both
experienced and novice users of AI technologies, ensuring
a diverse sample in terms of familiarity and adoption
levels. The student participants represented a wide range of
academic disciplines and grade levels, with varying
degrees of interest in technology and learning.
Data Collection
The data collection involved two main instruments:
1.
Teacher Survey: This survey assessed teachers'
adoption of AI tools, the perceived ease of use of these
tools, and their level of experience in using AI for teaching.
Items were adapted from the Technology Acceptance
Model (TAM) (Davis, 1989) to assess adoption and ease of
use.
2.
Student Interest Survey: This survey measured
students’ interest in learning through AI-based tools, using
a 5-point Likert scale to assess their engagement and
motivation.
3.
Teaching Effectiveness Evaluation: Both teachers
and students were asked to evaluate the effectiveness of
teaching. Teacher self-assessments included questions
about lesson delivery, student engagement, and learning
outcomes. Students provided feedback on how AI
impacted their learning experiences and engagement in
class.
Data Analysis
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Quantitative data were analyzed using Structural Equation
Modeling (SEM) to assess the relationships between AI
adoption, ease of use, teacher experience, student interest,
and teaching effectiveness. Moderating effects were tested
using interaction terms to explore how student interest
influenced the relationship between AI adoption and
teaching effectiveness.
RESULTS
AI Adoption and Teaching Effectiveness
The results indicated a strong positive relationship between
AI adoption and teaching effectiveness (β = 0.45, p < 0.01).
Teachers who actively adopted AI tools reported higher
levels of teaching effectiveness, particularly in terms of
student engagement and personalized learning.
Ease of Use and Teaching Effectiveness
Ease of use was also significantly related to teaching
effectiveness (β = 0.38, p < 0.05). Teachers who perceived
AI tools as easy to use were more likely to integrate them
into their teaching practices, leading to improved learning
outcomes. However, the ease of use was a stronger
predictor for novice users compared to experienced users,
who had developed strategies for overcoming technical
challenges.
Teachers’ Experience with AI
Teachers’ experience with AI had a significant positive
impact on teaching effectiveness (β = 0.42, p < 0.01).
Experienced teachers were able to leverage AI tools in
ways that enhanced their teaching strategies, resulting in
better student engagement and improved learning
outcomes.
Moderating Role of Student Interest
Student interest was found to significantly moderate the
relationship
between
AI
adoption
and
teaching
effectiveness (β = 0.34, p < 0.01). The positive impact of
AI adoption on teaching effectiveness was stronger among
students with high levels of interest in learning through AI
tools. In contrast, students with lower interest levels did not
experience significant improvements in learning outcomes,
despite the use of AI.
DISCUSSION
The findings highlight several important insights regarding
the integration of AI in teaching. First, the adoption of AI
tools, coupled with their ease of use and teachers’
experience, significantly enhances teaching effectiveness.
This suggests that AI can be a powerful tool in education
when implemented appropriately and with proper support
for teachers.
Second, the moderating role of student interest underscores
the importance of aligning AI tools with students' learning
preferences. AI tools are most effective when students are
motivated and interested in the technology, suggesting that
personalized learning experiences, which are a key feature
of AI, can enhance student engagement and performance.
Teachers' experience with AI emerged as a critical factor.
Experienced teachers are better able to navigate the
complexities of AI tools and integrate them effectively into
their teaching. For teachers who are less experienced,
training and professional development in AI are essential
to maximize the potential of these technologies.
CONCLUSION
This study demonstrates that AI adoption, ease of use, and
teachers’ experience all play crucial roles in enhancing
teaching effectiveness. Moreover, student interest in
learning through AI tools is a significant moderating factor,
amplifying the positive effects of AI integration. Schools
and educational institutions should prioritize teacher
training on AI technologies and ensure that AI tools are
aligned with students' interests to maximize their
effectiveness in improving teaching and learning
outcomes.
REFERENCES
Al Rajab, M., Odeh, S., Hazboun, S., & Alheeh, E. (2023).
AI-powered smart book: enhancing arabic education in
Palestine with augmented reality [Paper presentation].
International Symposium on Ambient Intelligence,
Guimaraes, Portugal. https://doi.org/10.1007/978-3-031-
43461-7_17
Allal-Chérif, O., Aránega, A. Y., & Sánchez, R. C. (2021).
Intelligent recruitment: How to identify, select, and retain
talents from around the world using artificial intelligence.
Technological Forecasting and Social Change, 169, 120-
822. https://doi.org/10.1016/j.techfore.2021.120822
CURRENT RESEARCH JOURNAL OF PEDAGOGICS (ISSN: 2767-3278)
https://masterjournals.com/index.php/crjp
4
Bhutoria, A. (2022). Personalized education and artificial
intelligence in the United States, China, and India: A
systematic review using a human-in-the-loop model.
Computers and Education: Artificial Intelligence, 3, 100-
168. https://doi.org/10.1016/j.caeai.2022.100068
Bowden, J. L.-H., Tickle, L., & Naumann, K. (2021). The
four pillars of tertiary student engagement and success: a
holistic measurement approach. Studies in Higher
Education,
46(6),
1207-1224.
https://doi.org/10.1080/03075079.2019.1672647
Calisto, F. M., Santiago, C., Nunes, N., & Nascimento, J.
C. (2021). Introduction of human-centric AI assistant to aid
radiologists for multimodal breast image classification.
International Journal of Human-Computer Studies, 150,
102-607. https://doi.org/10.1016/j.ijhcs.2021.102607
Choi, S., Jang, Y., & Kim, H. (2023). Influence of
pedagogical beliefs and perceived trust on teachers’
acceptance of educational artificial intelligence tools.
International Journal of Human–Computer Interaction,
39(4),
910-922.
https://doi.org/10.1080/10447318.2022.2049145
Chu, S. K. W., Reynolds, R. B., Tavares, N. J., Notari, M.,
& Lee, C. W. Y. (2021). 21st century skills development
through inquiry-based learning from theory to practice.
Springer.
Delgado, J. M. D., Oyedele, L., Demian, P., & Beach, T.
(2020). A research agenda for augmented and virtual
reality in architecture, engineering and construction.
Advanced
Engineering
Informatics,
45,
101-122.
https://doi.org/10.1016/j.aei.2020.101122
Demmans Epp, C., Daniel, B. K., & Muldner, K. (2023).
Learning analytics for supporting individualization: data-
informed adaptation of learning. Frontiers in Education, 8,
1240377. https://doi.org/10.3389/feduc.2023.1240377
Dimitriadou, E., & Lanitis, A. (2023). A critical evaluation,
challenges, and future perspectives of using artificial
intelligence and emerging technologies in smart
classrooms. Smart Learning Environments, 10(1), 12-21.
https://doi.org/10.1186/s40561-023-00231-3
Ebadi, S., & Amini, A. (2022). Examining the roles of
social presence and human-likeness on Iranian EFL
learners’
motivation
using
artificial
intelligence
technology: A case of CSIEC chatbot. Interactive Learning
Environments,
32(2),
655-673.
https://doi.org/10.1080/10494820.2022.2096638
Essel, H. B., Vlachopoulos, D., Tachie-Menson, A.,
Johnson, E. E., & Baah, P. K. (2022). The impact of a
virtual teaching assistant (chatbot) on students' learning in
Ghanaian higher education. International Journal of
Educational Technology in Higher Education, 19(1), 45-
57. https://doi.org/10.1186/s41239-022-00362-6
