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Anchor University Journal of Science and Technology , Volume 5 Issue 1 Suleiman
hps://dx.doi.org/10.4314/aujst.v5i1.8
Anchor University Journal of Science and Technology (AUJST)
A publication of the Faculty of Natural, Applied and Health Science, Anchor University Lagos
This study employed a quasi-experimental design to investigate the impact
of AI-Blended Learning and AI-Personalized Learning on undergraduate
biology students' attitudes and performance in climate change education.
The research addressed two research questions and tests two
corresponding null hypotheses. The population consists of 300 level under-
graduate biology students at Federal University Gusau, with a sample size
of 70 students selected through random sampling. Participants were
divided into three groups; AI-Blended Learning (20 students),
AI-Personalized Learning (20 students), and Traditional Classroom
Instruction (30 students). The intervention lasted four weeks. AI-Blended
Learning group used ChatGPT-3.5 alongside traditional classroom
instruction, while the AI-Personalized Learning group solely relied on
ChatGPT-3.5 for their instruction. Data were collected using two
instruments; the Climate Change Attitude Assessment (CCAA) and the
Climate Change Achievement Test (CCAT). Both CCAA and CCAT were
validated by experts and have reliability coefficients of 0.84 and 0.82,
respectively. Data collected were analyzed using mean and standard
deviation for the research questions, and Analysis of Covariance
(ANCOVA) tests for the null hypotheses at a significance level of 0.05.
Findings revealed that AI-Blended Learning significantly improved
students' attitudes and performance compared to AI-Personalized Learning
and Traditional Classroom Instruction. It is recommended that, lecturers
should adopt AI-Blended Learning with ChatGPT-3.5 to improve student
engagement and learning outcomes in environmental education.
Keywords: AI-Blended Learning, ChatGPT, AI-Personalized Learning,
Climate Change Education, Student Attitudes, performance
1. INTRODUCTION
The rapid advancement of artificial intelligence
(AI) technologies has revolutionized
educational practices, providing innovative
approaches to teaching and learning. Among
these, AI-Blended Learning and
AI-Personalized Learning have emerged as
significant methodologies that can potentially
enhance student engagement and academic
performance (Alshahrani, 2023; Chen et al.,
2020). AI-Blended Learning integrates AI tools
with traditional classroom instruction, offering
a hybrid model that combines the strengths of
both approaches (Park & Doo, 2024; Tong et
al., 2022). Conversely, AI-Personalized
Learning leverages AI algorithms to tailor
educational content and experiences to
individual student needs, promoting a more
customized learning environment (Hwang et al.
Impact of AI-Blended Learning and AI-Personalized Learning on Undergraduate
Biology Students' Attitude and Performance in Climate Change Education
Suleiman Sa’adu Matazu
Department of Science and
Vocational Education, Faculty of
Education and Extension Services,
Usmanu Danfodiyo University,
Sokoto Nigeria
Corresponding authors email:
saadu.matazu@udusok.edu.ng
Submitted 29 May 2024
Accepted 20 June 2024
Competing Interests.
The authors declare no
competing interests.
ABSTRACT
Vol. 5 No 1, August 2024, Pp. 83-95
83
URL: journal.aul.edu.ng
ISSN: 2736-0059 (Print); 2736-0067 (Online)
In AJOL: hps://www.ajol.info/index.php/aujst
Anchor University Journal of Science and Technology , Volume 5 Issue 1 Suleiman
hps://dx.doi.org/10.4314/aujst.v5i1.8
,2020).
Climate change education is an increasingly
critical component of the biology curriculum,
given the urgency of global environmental
issues (Monroe et al., 2019). Traditional
classroom instruction, often characterized by a
one-size-fits-all approach, may not adequately
address the diverse learning preferences and
needs of students, potentially limiting their
engagement and understanding of complex
topics such as climate change (Anderson, 2012;
Singh, 2021). Therefore, exploring the impact
of AI-enhanced learning methods on students'
attitudes and performance in climate change
education is essential for developing more
effective educational strategies.
Recent studies have highlighted the positive
effects of AI-Blended and AI-Personalized
Learning on various educational outcomes. For
instance, Park and Doo (2024), Ismail et al.
(2024), Tong et al. (2022) and Zawacki-Richter
et al. (2019) reported that AI-Blended Learning
environments could improve student motivation
and conceptual understanding in science
subjects. Similarly, research by Holmes et al.
(2019) demonstrated that AI-Personalized
Learning could significantly enhance academic
performance by providing adaptive feedback
and personalized learning pathways.
ChatGPT is a powerful language model
Chatbot developed by OpenAI, a leading
research organization focused on advancing
artificial intelligence technologies. ChatGPT is
capable of generating human-like text based on
input. Chatbots are used in education to provide
instant feedback, facilitate learning, and
support administrative tasks (Okonkwo &
Ade-Ibijola, 2021). These AI-driven tools like
ChatGPT can simulate human conversations,
making them valuable for tutoring, answering
student queries, and creating interactive
learning environments. Examples include
AI-powered tutoring systems and virtual teach-
ing assistants that enhance student engagement
and learning outcomes (Chen et al., 2020). This
study aims to investigate the impact of
AI-Blended Learning and AI-Personalized
Learning on undergraduate biology students'
attitudes and performance in climate change
education, compared to traditional classroom
instruction.
1.1. Review of Related Literature
Artificial intelligence has progressively
permeated various sectors, and education is no
exception. AI in education encompasses a
range of applications, from intelligent tutoring
systems and automated grading to predictive
analytics and personalized learning
environments. AI technologies offer the
potential to transform traditional educational
practices by enhancing the personalization of
learning experiences, optimizing administrative
tasks, and providing data-driven insights for
decision-making (Holmes et al., 2019; Ismail et
al., 2024; Tong et al., 2022). According to
Chen et al. (2020), AI-driven adaptive learning
systems can adjust content delivery based on
student performance and engagement, thereby
supporting individualized learning paths and
potentially improving educational outcomes.
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AI applications in education can broadly be
categorized into three main areas:
administrative support, instructional support,
and student support. Administrative support
involves automating routine tasks such as
scheduling, attendance tracking, and grading,
which can free up teachers to focus more on
teaching and interacting with students
(Sangheethaa & Korath, 2024;
Zawacki-Richter et al., 2019). Instructional
support includes AI-powered tools that assist
teachers in creating and delivering content,
such as intelligent tutoring systems and virtual
teaching assistants (Ismail et al., 2024; Luckin
et al., 2016). Finally, student support AI
applications in education can broadly be
categorized into three main areas:
administrative support, instructional support,
and student support. Administrative support
involves automating routine tasks such as
scheduling, attendance tracking, and grading,
which can free up teachers to focus more on
teaching and interacting with students
(Sangheethaa & Korath, 2024;
Zawacki-Richter et al., 2019). Instructional
support includes AI-powered tools that assist
teachers in creating and delivering content,
such as intelligent tutoring systems and virtual
teaching assistants (Ismail et al., 2024; Luckin
et al., 2016). Finally, student support
encompasses personalized learning
environments that adapt to individual learners'
needs, providing tailored feedback and
resources to enhance the learning experience
(Kulik & Fletcher, 2016).
Blended learning, which combines traditional
face-to-face instruction with online learning
activities, has been significantly enhanced by
AI technologies. AI-blended learning
environments leverage the capabilities of AI to
create more interactive and personalized
learning experiences (Alshahrani, 2023). In
science education, AI-blended learning can
facilitate the integration of virtual labs,
simulations, and interactive tutorials, which
can enhance students' conceptual
understanding and engagement (Ismail et al.,
2024). Research has shown that AI-blended
learning can improve student outcomes in
science education. For example, a study by
Tong et al. (2022), Sangheethaa and Korath
(2024), and Wu et al. (2010) found that
students who participated in an AI-blended
learning environment demonstrated higher
levels of engagement and better academic
performance compared to those in traditional
classroom settings. The AI components, such
as real-time feedback and adaptive learning
pathways, enabled students to grasp complex
scientific concepts more effectively and at their
own pace.
Moreover, according to Wu et al. (2010),
AI-blended learning can support collaborative
learning and critical thinking skills, which are
essential in science education. Through
AI-powered discussion forums, peer
assessment tools, and collaborative projects,
students can engage in meaningful interactions
with their peers and instructors, fostering a
deeper understanding of scientific principles
(Alshahrani, 2023; Hwang et al., 2020). The
integration of AI in blended learning also
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allows for the continuous monitoring and
assessment of student progress, enabling
teachers to identify and address learning gaps
promptly (Alshahrani, 2023; Sangheethaa &
Korath, 2024; Tong et al., 2022).
AI-personalized learning approaches are
designed to cater to the individual learning
needs and preferences of students. These
approaches utilize AI algorithms to analyze
student data, such as learning behaviors,
performance metrics, and engagement levels,
to create customized learning experiences. The
goal is to provide each student with the most
appropriate resources, activities, and feedback
to optimize their learning outcomes (Chen et
al., 2020). One of the key benefits of
AI-personalized learning is its ability to offer
real-time adaptive feedback. For instance,
intelligent tutoring systems can provide imme-
diate feedback on student performance, helping
learners understand their mistakes and correct
them promptly (Magomadov, 2020; Woolf et
al., 2013). This continuous and personalized
feedback loop can enhance student motivation
and self-efficacy, as they receive support that
is tailored to their specific needs and progress.
AI-personalized learning approaches have been
shown to be particularly effective in subjects
that require a high degree of individualized
instruction, such as mathematics and science.
Studies have demonstrated that students in
AI-personalized learning environments tend to
perform better academically compared to those
in traditional settings. For example, a study by
Magomadov (2020) and Roschelle et al. (2016)
found that students who used an AI-powered
personalized learning platform for mathematics
achieved significant gains in their test scores
compared to a control group. Furthermore,
AI-personalized learning can support the
development of 21st-century skills, such as
critical thinking, problem-solving, and
self-directed learning. By providing learners
with personalized learning paths and resources,
AI can help students develop these essential
skills in a more targeted and efficient manner
(Luckin et al., 2016). The use of AI in
personalized learning also promotes equity in
education, as it can provide additional support
to students who may be struggling or have
diverse learning needs (Holmes et al., 2019).
Traditional classroom instruction in climate
change education typically involves lectures,
textbook readings, and standardized
assessments. While this approach provides a
foundational understanding of climate science,
it may not fully engage students or address
their diverse learning needs (Anderson, 2012).
Traditional methods often rely on passive
learning, where students are expected to absorb
information without actively engaging with the
content or applying their knowledge to
real-world scenarios. Research has highlighted
several limitations of traditional classroom
instruction in climate change education. For
example, Monroe et al. (2019) noted that
traditional methods might not effectively
convey the complexity and urgency of climate
change, leading to a lack of student motivation
and interest. Similarly, the one-size-fits-all
approach may not cater for the diverse learning
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preferences of students, resulting in varying
levels of understanding and engagement
(Singh, 2021).
However, traditional classroom instruction
remains an important component of climate
change education, as it provides a structured
learning environment and access to expert
knowledge (Singh, 2021). Teachers can
enhance traditional methods by incorporating
interactive and experiential learning activities,
such as field trips, experiments, and
discussions, to make the content more relevant
and engaging for students (Anderson, 2012;
Matazu & Isma’il, 2023). The integration of AI
technologies with traditional instruction can
also address some of these limitations by
providing personalized and adaptive learning
experiences.
Educational interventions, such as AI-enhanced
learning methods, have been shown to
positively impact student attitudes and
performance. AI-blended and AI-personalized
learning approaches can create more engaging
and effective learning experiences, leading to
improved academic outcomes and student
satisfaction (Alshahrani, 2023; Chen et al.,
2020). These interventions can also influence
students' attitudes towards the subject matter,
increasing their interest and motivation to
learn.
Studies have demonstrated that AI-enhanced
learning environments can lead to higher levels
of student engagement and achievement. For
instance, Zawacki-Richter et al. (2019) and Wu
et al. (2010) found that students in AI-blended
learning environments reported greater
satisfaction with their learning experiences and
achieved better academic results compared to
those in traditional settings. The interactive and
personalized nature of AI-enhanced learning
can make the content more accessible and
relevant to students, fostering a positive
attitude towards the subject.
Moreover, AI-personalized learning
approaches can address individual learning
needs and preferences, resulting in more
equitable educational outcomes. Research by
Holmes et al. (2019) indicates that students
who receive personalized support through AI
technologies are more likely to succeed
academically and develop a positive attitude
towards learning. This is particularly important
in climate change education, where students
may have varying levels of prior knowledge
and interest.
1.2. Statement of the Problem
The growing recognition of the importance of
climate change education has not translated
into effective engagement of students to fully
address their diverse learning needs within
traditional classroom instruction. This
inadequacy is particularly pronounced in
undergraduate biology courses, where profound
understanding of climate change is imperative.
The one-size-fits-all approach of traditional
classroom instruction may limit student
motivation and attitudes towards complex
environmental issues. Emerging AI-enhanced
learning methods, such as AI-Blended
Learning and AI-Personalized Learning, offer
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potential solutions by providing interactive and
customized learning experiences. However,
there is a lack of empirical research examining
the effectiveness of these AI-based approaches
compared to traditional instruction. Therefore,
the main objective of this study is to investigate
the impact of AI-Blended Learning and
AI-Personalized Learning on undergraduate
biology students' attitudes and performance in
climate change education.
1.3. Research Questions
The study was guided by the following question;
What are the effects of AI-Blended
Learning, AI-Personalized Learning, and
Traditional Classroom Instruction on
students' attitudes towards climate
change?
What are the effects of AI-Blended
Learning, AI-Personalized Learning, and
Traditional Classroom Instruction on
students' performance in climate change
education?
1.4. Null Hypotheses
The following null hypotheses were formulated
for the study;
There is no significant difference in the
mean scores of students’ attitude towards
climate change among AI-Blended
Learning, AI-Personalized Learning, and
Traditional Classroom Instruction.
There is no significant difference in the mean
performance scores of students in AI-Blended
Learning, AI-Personalized Learning, and
Traditional Classroom Instruction in climate
change education.
Methodology
This study employed a quasi-experimental
design with a pretest-posttest control group
design. The study included 70 undergraduate
biology students from Federal University Gusau.
The students were divided into three groups
namely AI-Blended Learning (n=20),
AI-Personalized Learning (n=20), and
Traditional Classroom Instruction (n=30). The
participants were assigned to one of the three
instructional groups through random sampling
from existing classes of 300-level Biological
Science students at the university.
The participants in the AI-Blended Learning
group combined traditional classroom
instruction (face-to-face teaching) with
AI-enhanced online activities that provide
real-time feedback mechanisms to engage them
to augment their understanding of climate
change concepts. AI-Personalized Learning
group experienced a fully AI-driven
personalized learning environment. An
intelligent tutoring system adapted content and
activities based on individual engagement levels,
offering personalized feedback and personalized
learning paths. This group were only given the
topics they will covered within the time frame of
the research. Participants in both the AI-Blended
Learning and AI-Personalized Learning groups
used ChatGPT-3.5, a free AI developed by
OpenAI, for the intervention. The participants in
the Traditional Classroom Instruction group
followed conventional classroom instruction.
This involved a series of face-to-face lectures
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with no intervention. This group served as a
control group for comparison.
A questionnaire tagged Climate Change
Attitude Assessment (CCAA) was used to
assess students' attitudes towards climate
change across four dimensions, namely;
awareness, concern, perceived importance, and
willingness to take action. The CCAA was rated
on a 4-point Likert scale. It was validated
through expert review and pilot testing, with a
reliability score of 0.85 using Cronbach's alpha.
A mean score of 2.5 or above indicates a
positive attitude towards climate change.
Another instrument tagged Climate Change
Achievement Test (CCAT) was constructed.
The CCAT consisted of multiple-choice and
short-answer questions designed to evaluate
students' knowledge of climate change
education. It was validated by experts.
Reliability of CCAT was evaluated with
Pearson Product-Moment Correlation, resulting
in a coefficient of 0.82, by test-retest.
The climate change topics covered by all the
groups were, Introduction to Climate Change,
Causes of Climate Change, Impacts of Climate
Change, Mitigation Strategies, Global and
Local Perspectives on Climate change and
Climate Change Policies and Actions. A week
before the beginning of the intervention, student
participants in all groups completed a pre-test
using the CCAA and CCAT. After the four
weeks’ instructional period, the participants
completed a post-test to measure changes in
attitudes and performance. Data collected were
analyzed using descriptive statistics (mean and
standard deviation) and inferential statistics
(Analysis of Covariance (ANCOVA)) at a
significance level of 0.05, with post hoc tests
specifying group differences. Statistical
Package for Social Sciences (SPSS) version 23
was used for the analyses.
Results
Research Question One: What are the effects
of AI-Blended Learning, AI-Personalized
Learning, and Traditional Classroom
Instruction on students' attitudes towards
climate change?
Table 1 revealed that, AI-Blended Learning
resulted in a substantial increase in students'
attitudes towards climate change (mean gain
score = 32.41), higher than AI-Personalized
Learning (mean gain score = 5.16) and
Traditional Classroom Instruction (mean gain
score = 3.94). AI-Personalized Learning also
showed a higher gain compared to Traditional
Classroom Instruction.
Null Hypothesis One (H01): There is no
significant difference in the mean attitudinal
scores of students towards climate change
among AI-Blended Learning, AI-Personalized
Learning, and Traditional Classroom
Instruction.
The ANCOVA results in Table 2a revealed sig-
nificant effect of instructional groups (AI-
Blended Learning, AI-Personalized Learning,
and Traditional Classroom Instruction) on stu-
dents' posttest scores in attitudes towards cli-
mate change, while controlling for pretest
scores, F(2, 67) = 144.050, p < .001, η² = 0.766.
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Table 1: Mean scores of the instructional groups on students' attitudes towar ds
climate change
Instructional Groups N Pretest Posttest Mean Gain
Score
Mean SD Mean SD
AI-Blended Learning 20 45.18 2.87 77.59 3.12 32.41
AI-Personalized Learn-
ing
20 46.89 3.05 52.05 2.94 5.16
Traditional Classroom
Instruction
30 45.97 2.91 49.91 3.08 3.94
Table 2a: ANCOVA results on mean attitudinal scores among the thr ee instructional
groups
Source Type II Sum of
Square df Mean Square F Value Sig. Partial Eta
Squared
Corrected
Model 1024.320 3 341.440 109.996 .000 0.766
Intercept 119.647 1 119.647 38.498 .000 0.575
Covariate
(Pretest) 10.065 1 10.065 3.240 .076 0.097
Group 894.608 2 447.304 144.050 .000 0.681
Error 314.073 67 4.691
Total 1368.713 71
Corrected To-
tal 1338.393 70
Treatments AI-Blended
Learning
AI-
Personalized
Learning Traditional Classroom Instruction
AI-Blended
Learning - 27.25* 28.47*
AI-Personalized
Learning 27.25* - 5.22
Traditional
Classroom In-
struction
28.47* 5.22 -
Table 2b: Summary of Scheffé's Post Hoc Test for Attitudes Towards Climate Change
* denotes pairs of groups that are significantly different (p < 0.05)
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91
Therefore, H01 is rejected, indicating a signifi-
cant difference in the mean attitudinal scores
of students towards climate change among the
three instructional groups. To determine which
specific groups differ, Scheffé's Post hoc test
was conducted (see Table 2b).
The summary of Scheffé's post hoc test in
Table 2b revealed that AI-Blended Learning
significantly improved students' attitudes
towards climate change compared to
AI-Personalized Learning and Traditional
Classroom Instruction (p < 0.05). No
significant difference was found between
AI-Personalized Learning and Traditional
Classroom Instruction.
Research Question Two: What are the
effects of AI-Blended Learning,
AI-Personalized Learning, and Traditional
Classroom Instruction on students'
performance in climate change education?
Table 3 revealed the effects of instructional
methods on students' performance in climate
change education were AI-Blended Learning
substantially improved in posttest scores
(Mean = 27.57, SD = 4.12), demonstrating a
mean gain score of 11.69. AI-Personalized
Learning (Mean = 16.50, SD = 3.75) and Tra-
ditional Classroom Instruction (Mean = 19.01,
SD = 3.89) showed smaller improvements,
with mean gain scores of 0.03 and 3.01,
respectively.
Null Hypothesis Two (H02): There is no
significant difference in the mean performance
scores of students in AI-Blended Learning,
AI-Personalized Learning, and Traditional
Classroom Instruction in climate change
education.
The ANCOVA results in Table 4a revealed a
significant effect of instructional groups on
students' posttest scores, controlling for pretest
scores, F(2, 67) = 42.281, p < 0.001, η² = 0.664.
On the basis of this, H02 was rejected, that
there is a significant difference in the mean
performance scores of students among
AI-Blended Learning, AI-Personalized
Learning, and Traditional Classroom
Instruction in climate change education. Thus,
determine which specific groups differ,
Scheffé's Post hoc test was conducted as
shown in Table 2b.
The summary of Scheffe's post hoc test in
Table 4b revealed that AI-Blended Learning
significantly improved mean gain scores
compared to AI-Personalized Learning (mean
difference = 11.66, p < 0.05) and Traditional
Classroom Instruction (mean difference =
8.68, p < 0.05). AI-Personalized Learning
showed no significant difference compared to
Traditional Classroom Instruction (mean
difference = 2.98, p > 0.05).
DISCUSSION
The findings related to Research Question One
and Null Hypothesis One, as indicated in Ta-
ble 1, revealed that AI-Blended Learning
demonstrated the most substantial increase in
mean score from pre-test to post-test, indicat-
ing a positive impact on attitudes towards cli-
mate change. AI-Personalized Learning and
Traditional Classroom Instruction also showed
increases, though to a lesser extent.
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Table 3: Descriptive statistics for students' perfor mance in climate change education
Instructional Groups N Pretest Posttest Mean Gain
Score
Mean SD Mean SD
AI-Blended Learning 20 15.88 4.12 27.57 4.12 11.69
AI-Personalized Learning 20 16.47 3.75 16.50 3.75 0.03
Traditional Classroom
Instruction
30 16.00 3.89 19.01 3.89 3.01
Table 4a: ANOVA Results for Per formance Scores in Climate Change Education
Source Type II SS df Mean Square F Sig Partial Eta
Squared
Corrected Model 362.454 3 120.818 37.279 <0.001 0.707
Intercept 87.576 1 87.576 27.018 <0.001 0.569
Covariate (Pre-test) 0.292 1 0.292 0.090 0.765 0.003
Group 274.586 2 137.293 42.281 <0.001 0.664
Error 149.153 67 2.228
Total 1061.790 71
Corrected Total 511.607 70
Treatment
AI-
Blended
Learn-
ing
AI-Personalized
Learning Traditional Classroom Instruction
AI-Blended
Learning - 11.66* 8.68*
AI-
Personalized
Learning
11.66* - 2.98
Traditional
Classroom
Instruction
8.68* 2.98 -
* denotes pairs of groups that are significantly different (p < 0.05)
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The significant difference in students' attitudes
towards climate change among the three
instructional groups, supported by the
ANCOVA result (Table 2a), rejects the H01 that
there is no significant difference in the mean
attitudinal levels of students towards climate
change among AI-Blended Learning,
AI-Personalized Learning, and Traditional
Classroom Instruction. Post hoc analysis using
the Scheffe's post hoc test (Table 2b) further
showed AI-Blended Learning significantly
differed from AI-Personalized Learning and
Traditional Classroom Instruction in impacting
attitudes towards climate change. This finding
is consistent with studies by Smith (2020), who
reported the potential of AI-Blended Learning
in promoting positive environmental attitudes.
AI-Blended Learning, utilizing ChatGPT-.3.5,
can personalize learning experiences by
adapting content and pacing to match students'
learning styles and interests. This personalized
approach may engage students more effectively
with climate change issues, nurturing deeper
understanding and positive attitudes.
Table 3 presented descriptive statistics for
students' performance in climate change
education across AI-Blended Learning,
AI-Personalized Learning, and Traditional
Classroom Instruction. AI-Blended Learning
achieved the highest mean performance score,
suggesting superior effectiveness compared to
AI-Personalized Learning and Traditional
Classroom Instruction. The standard deviations
indicate moderate variability in scores for all
groups. The significant difference in mean
performance scores among the three
instructional groups, as evidenced by the
ANCOVA results (Table 4a), supports the
rejection of the null hypothesis (H02). Scheffe's
post hoc test (Table 4b) further clarified that AI
-Blended Learning significantly outperformed
AI-Personalized Learning and Traditional
Classroom Instruction in impacting student
performance. This stresses the effectiveness of
ChatGPT-3.5 in AI-Blended Learning in
improving knowledge and understanding of
climate change concepts. It also underscores the
potential of AI technologies like ChatGPT in
enhancing educational learning outcomes,
aligning with previous research by Brown and
Jones (2021) and Park and Doo (2024)
indicating that AI-Blended Learning, utilizing
technologies such as ChatGPT, can improve
student performance by providing personalized
and adaptive learning experiences.
Conclusion
The findings of this study indicate that
AI-Blended Learning, particularly when
integrated with ChatGPT-3.5, significantly
improved students' attitudes and performance in
various aspects of climate change education
compared to AI-Personalized Learning and
Traditional Classroom Instruction. This could
be attributed to the fact that, AI-Blended
Learning combines the advantages of both
online utilizations of ChatGPT-3.5 and
traditional classroom instruction, offering
flexibility and accessibility while maintaining
teacher-student interaction. It may also cater to
students' learning styles by adapting content
and pacing to match their individual needs and
preferences. These results indicated the
effectiveness of ChatGPT in improving
educational outcomes through teacher
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guidance, providing personalized learning
experiences that engage students effectively
with complex topics like climate change.
Therefore, the study recommends the
followings:
1. Higher education lecturers should
integrate AI-Blended Learning, utiliz-
ing ChatGPT, alongside traditional
classroom instruction to enhance stu-
dent engagement and improve learning
outcomes.
2. Students should actively utilize AI tools
like ChatGPT to supplement their
traditional classroom learning,
especially for understanding complex
topics. They should also provide
feedback to their teachers to enhance
the use of AI in education.
3. Researchers should conduct more studies
to investigate the impact of AI-Blended
Learning on students' learning
outcomes, particularly in complex
subjects.
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