# Generative AI as a Learning Buddy and Teaching Assistant: Pre-service
Teachers’ Uses and Attitudes

Matthew Nyaaba1, Lehong Shi2, Macharious Nabang3, Xiaoming Zhai4, Patrick
Kyeremeh5, Samuel Arthur Ayoberd6, Bismark Nyaaba Akanzire7
1AI4STEM Education Center & Department of Educational Theory and Practice,
University of Georgia, USA
2Department of Workforce Education and Instructional Technology, University of
Georgia, USA
3Department of Creative Arts, Bagabaga College of Education, Tamale, Ghana
4AI4STEM Education Center & Department of Mathematics, Science, and Social
Studies Education, University of Georgia, USA
5Department of Mathematics & I.C.T., St. Joseph’s College of Education,
Bechem, Ghana
6Department of Science Education, University for Development Studies, Tamale,
Ghana
7Department of Education, Gambaga College of Education, Gambaga, Ghana
Corresponding Author<EMAIL_ADDRESS><EMAIL_ADDRESS>
###### Abstract

To uncover pre-service teachers’ (PSTs’) user experience and perceptions of
generative artificial intelligence (GenAI) applications, we surveyed 167 Ghana
PSTs’ specific uses of GenAI as the learning buddy and teaching assistant and
their attitudes towards these applications. Employing exploratory factor
analysis (EFA), we identified three key factors shaping PSTs’ attitudes toward
GenAI, teaching, learning, and ethical and advocacy factors. The mean scores
of these factors revealed a generally positive attitude towards GenAI, showing
high levels of agreement on GenAI’s potential to enhance PSTs’ content
knowledge and access to learning and teaching resources, this, in turn,
reduces their need for assistance from colleagues in their learning and
teaching practices. The findings specifically showed that PSTs use GenAI as a
learning buddy to access reading materials, in-depth content explanations, and
practical examples and as a teaching assistant to enhance their teaching
resources, developing assessment strategies and lesson planning. A regression
analysis showed that background factors such as age, gender, and year of study
do not predict PSTs’ attitude towards GenAI, but the age and year of study
significantly predict the frequency of their use of GenAI, whereas gender does
not. These findings indicate that older PSTs and those further along in their
teacher education programs may use GenAI more frequently, but their
perceptions of the application remain unchanged. However, the PSTs were
concerned about the accuracy and trustworthiness of the information provided
by GenAI applications. We, therefore, suggest the need to address the concerns
about the accuracy and trustworthiness of GenAI, ensuring that PSTs can
confidently rely on these applications in their teacher preparation programs.
Additionally, we recommend targeted strategies to integrate GenAI more
effectively into both the learning and teaching processes for PSTs.

###### Index Terms:

Generative Artificial Intelligence (GenAI), AI, Use, Attitude, Learning,
Teaching, GenAI.

##  I Introduction

Generative artificial intelligence (GenAI) is artificial intelligence capable
of generating new text, images, videos, and other data by responding to human
inquiry [1]. GenAI has recently emerged as a promising resource within
educational contexts, including teacher education programs. Numerous studies
highlight its potential to enhance personalized learning, lesson preparation,
and research for pre-service teachers (PSTs) [2, 3]. GenAI applications can
provide real-time feedback, simulate classroom scenarios, and support the
development of digital literacy skills, making them valuable assets in the
preparation of future educators [4, 5]. By offering tailored educational
experiences, PSTs can better understand and apply theoretical concepts to
practical use, ultimately improving their academic performance and readiness
for the teaching profession. This becomes significant as PSTs possess dual
roles both as learners and teachers, which complicates their user experience
of GenAI integration.

Research indicates that PSTs utilize GenAI applications in their coursework
learning and teaching practice and recognize their potential benefits [6, 7].
However, recent studies also show that the effective use of GenAI applications
by PSTs is often hindered by various challenges, such as a lack of training in
GenAI integration, the lack of skills to handle ethical and algorithmic
biases, and negative attitudes [8]. These challenges lead them to have
skepticism toward the use of GenAI applications in their learning and
teaching.

Therefore, it is vital and urgent to understand how PSTs use GenAI
applications in their learning and practical fields and their attitudes toward
GenAI integration. This study explores the various aspects that GenAI
applications assist PSTs in their coursework by serving as a learning buddy,
how GenAI assists PSTs in their teaching practice by serving as a teaching
assistant, and their attitudes towards GenAI. The study further examines the
various background factors that predict PSTs attitudes and their use of GenAI
as their learning buddy and teaching assistant. The findings present a
comprehensive understanding of the dual user experience of GenAI towards the
holistic professional development of PSTs in their teacher preparation
program. The study answers the following research questions:

  1. 1.

In what ways do PSTs use GenAI as a learning buddy during their coursework?

  2. 2.

How do PSTs employ GenAI as a teaching assistant during their teaching
practice?

  3. 3.

What is the attitude of PSTs towards GenAI applications?

  4. 4.

How do the PSTs’ background factors predict their attitudes toward GenAI and
the uses of GenAI for learning and teaching?

##  II Theoretical Framework: The Dual Role of Generative AI for Pre-service
Teachers

Pre-service teachers (PSTs), positioned uniquely as both learners and
educators, are increasingly integrating GenAI into their daily practices [3].
These dual roles allow them to exploit GenAI’s capacities as a learning buddy,
providing personalized learning support, facilitating deeper understanding
through interactive dialogues, and offering instant feedback on various tasks.
Meanwhile, PSTs leverage GenAI as a teaching assistant, which aids in creating
instructional materials, grading homework and assessments, and even simulating
classroom interactions to enhance pedagogical skills. These opportunities
offered by GenAI not only enrich PSTs’ learning experience but also facilitate
the development of their instructional strategies, promoting an adaptive and
innovative educational environment.

###  II-A Generative AI as a Learning Buddy

When GenAI plays the role of a learning buddy, it can facilitate PSTs’
learning activities by offering personalized instruction, real-time feedback,
and interactive engagement, essential for skill acquisition and conceptual
understanding [9, 10]. First, GenAI offers a transformative potential for PSTs
by extending their perspectives and knowledge. This technology harnesses vast
datasets to provide access to diverse viewpoints, comprehensive information,
and various educational paradigms beyond traditional textbooks and lectures
[11]. PSTs can leverage GenAI to explore a multitude of educational theories,
pedagogical methods, and cultural contexts, enriching their understanding and
broadening their intellectual horizons [12]. By presenting nuanced
perspectives on complex educational issues, GenAI encourages critical thinking
and helps future educators develop a more inclusive and multifaceted approach
to teaching [13].

In addition to broadening perspectives, GenAI excels in offering in-depth
content explanations. PSTs often grapple with complex theories and concepts
that require detailed, tailored explanations [14, 15]. GenAI-powered
applications can break down intricate subjects into more digestible segments,
providing explanations customized to the learner’s pace and level of
understanding [16]. This individualized learning approach ensures that PSTs
can thoroughly comprehend essential educational theories and practices [17,
18]. GenAI can also facilitate a dynamic learning environment where PSTs can
ask questions and receive immediate, detailed feedback [19], enhancing their
grasp of challenging content and promoting a deeper engagement with the
material.

GenAI also plays a crucial role in finding practical examples illustrating
theoretical concepts, which is vital for effective teacher training. By
synthesizing a wide array of real-world scenarios and case studies, GenAI can
provide PSTs with relevant examples demonstrating the application of
educational theories in practice [20]. This contextual learning bridges the
gap between theory and practice, making abstract ideas more tangible and
easier to comprehend. Furthermore, GenAI continually updates its repository of
examples, ensuring that PSTs are exposed to the latest developments and best
practices in the field of education [3]. This exposure helps them stay current
with contemporary educational trends and methodologies [21, 22].

Another significant advantage of GenAI is its ability to assist PSTs in
finding relevant reading materials. GenAI can efficiently search through vast
digital libraries and databases to curate lists of scholarly articles, books,
and other resources tailored to specific learning needs and interests [23].
This capability saves time and ensures that PSTs have access to high-quality,
up-to-date literature essential for their academic and professional
development [21]. By providing a curated selection of reading materials, GenAI
supports PSTs in building a robust theoretical foundation and staying informed
about current research and debates in education.

Finally, GenAI can greatly enhance the reflective practices of PSTs, which are
crucial for their professional growth [24]. GenAI applications can facilitate
structured reflection by prompting PSTs to analyze their experiences, assess
their understanding, and identify areas for improvement [25]. These
applications can also provide feedback on reflective writings, suggesting
further areas for contemplation and helping PSTs develop critical self-
awareness [26, 27]. By guiding reflective practices, GenAI fosters a habit of
continuous self-assessment and improvement, which is essential for effective
teaching. Moreover, GenAI can compile and analyze data from multiple
reflections over time [19, 24], offering insights into PSTs’ development and
progression, thus supporting ongoing professional growth and effective
practice.

Despite the great potential of GenAI for PSTs’ learning, there is a need for
well-defined guidelines and policies to support the responsible and effective
integration of GenAI in learning [28, 29]. Holland and Ciachir [28] posited
that learners appreciated the immediacy and validation provided by GenAI, but
concerns about equity and academic integrity emerged, particularly in group
work where misuse of the application could lead to academic misconduct.
Likewise, Rasul et al. [29] identified academic integrity as a significant
challenge in their examination of GenAI roles in higher education and
emphasized the need for clear institutional policies and transparency to
mitigate these risks and ensure ethical GenAI uses.

###  II-B Generative AI as a Teaching Assistant

As a teaching assistant, GenAI offers significant advantages for PSTs in
identifying effective assessment strategies. Traditional assessment methods
often lack the flexibility and adaptability needed to address diverse student
needs and learning styles [30]. GenAI can analyze extensive educational data
to recommend varied assessment techniques, such as performance-based tasks and
adaptive testing. These AI-driven strategies ensure a more holistic evaluation
of student understanding and progress. By leveraging GenAI to develop and
implement diverse assessment strategies, PSTs can gain a deeper insight into
student learning [2, 31], allowing them to tailor their instructional
approaches to better meet individual student needs and improve educational
outcomes.

In addition to enhancing assessment strategies, GenAI can assist PSTs in
identifying clear and measurable lesson objectives. Establishing precise
objectives is critical for effective lesson planning and delivery, as it
ensures that lessons are aligned with educational standards and student
learning goals [2, 27]. GenAI can leverage its powerful language analysis and
generation capacity to analyze curriculum guidelines, educational standards,
and student data to suggest specific and actionable objectives for each lesson
[32]. This process not only helps PSTs focus their instruction on key learning
outcomes but also provides a structured framework for evaluating student
progress. By using GenAI to refine lesson objectives, PSTs can ensure their
teaching is purposeful and aligned with broader educational goals.

Creating comprehensive and effective lesson plans is another area where GenAI
can significantly aid PSTs. Recent studies have reported that AI-powered
systems can generate detailed lesson plans incorporating best practices in
pedagogy, differentiated instruction, and classroom management [2]. These
plans can include various instructional strategies, activities, and assessment
methods, such as analogies [33], designed to engage students and promote deep
understanding. By automating the lesson planning process, GenAI allows PSTs to
concentrate more on delivering high-quality instruction and addressing the
dynamic needs of their students. Additionally, GenAI-generated lesson plans
can be easily adapted and personalized, ensuring they remain relevant and
effective in diverse classroom settings.

GenAI is also invaluable in providing exemplary lesson samples to PSTs. By
analyzing a vast repository of lesson plans and educational resources, GenAI
can curate a selection of high-quality lesson examples aligned with specific
learning objectives and curriculum standards [34]. These samples serve as
valuable references, offering practical guidance and inspiration for designing
effective lessons. Access to a diverse array of exemplary lessons helps PSTs
expand their instructional strategies and ensures they are equipped with the
applications needed to deliver engaging and impactful instruction [2]. This
exposure to well-crafted lesson samples enhances the pedagogical skills of
PSTs and boosts their confidence in lesson planning and execution [35].

Finally, GenAI can aid PSTs in finding a wide range of teaching resources
[23]. From interactive multimedia content to scholarly articles and
educational games, GenAI can sift through extensive digital libraries and
databases to recommend resources that support various aspects of teaching,
learning, and research [36, 3]. This capability not only saves PSTs’ time but
also ensures them having access to the most relevant and up-to-date materials
available. By integrating these resources into their teaching, PSTs can
enhance the learning experience, cater to different learning styles, and keep
students engaged. The continuous updating of AI databases with new and
innovative teaching resources ensures that PSTs always have access to fresh,
high-quality materials to enrich their instruction.

##  III Method

In this study, we employed a quantitative survey approach [37, 38] to
systematically investigate the extent to which PSTs engaged with GenAI
applications as a learning buddy during their academic coursework and as a
teaching assistant during their teaching practice. This approach was not only
relevant to addressing the research questions but also appropriate for
reaching a larger number of participants to gain insights, especially since
they were in different institutions [38].

###  III-A Participants

Utilizing a convenience sampling approach, we recruited 167 pre-service
teachers (PSTs) as participants from four teacher education institutions in
Ghana to complete an online survey. These PSTs are enrolled in a four-year
bachelor’s degree program in teacher education, specializing in high school
education. The selection of these institutions was deliberate and guided by
considerations of convenience for the researchers. The demographic breakdowns
of PSTs showed that most of them fall within the 21-25 age group, comprising
61.82% of the sample, followed by the 26-30 age group at 32.12%. The youngest
(16-20) and oldest (31-35) age groups are the least represented, with 2.42%
and 3.64%, respectively. There is a significant disparity regarding their
gender, with males constituting 73.05% and females 26.95%. In terms of the
year in their program (level), the largest group was in Level 400 (i.e., year
of study), making up 57.49% of the sample, followed by those at Level 300 with
35.93%, and then Level 200 level representing at 6.59%.

###  III-B Instruments

We iteratively developed a survey instrument to assess PSTs’ use and attitudes
towards GenAI in their learning and teaching. The instrument consisted of four
sections with 22 items inspired by the works of Strzelecki (2023) on the
‘ChatGPT acceptance and use scale.’

The first section contained three items to collect participants’ background
information (i.e., age, gender, and year in program-level). The second section
contained two open-ended items to obtain the areas participants use GenAI
applications for their learning and in their teaching (e.g., “Which aspect do
you think GenAI is or will be most useful in your teaching?”). The third
section contained two items to solicit the frequency at which participants use
GenAI within a week (e.g., “How often do you use GenAI for teaching?” “How
often do you use GenAI for learning purposes?”). These items were made up of
five categories (e.g., never, rarely, sometimes, often, and very often).

The fourth section was developed to measure participants’ attitudes towards
using GenAI in both their learning and teaching, consisting of 12 items. The
development of the attitude scale items was specifically adapted from the
‘ChatGPT acceptance and use scale’ by Strzelecki (2023). The scale originally
contained eight constructs; however, we adopted items from five constructs
relevant to our study to assess “GenAI use and attitude,” including
“performance expectancy” which consisted of four items, “Effort expectancy”
containing four items, “Social influence” with three items, “Facilitating
Conditions” with four items, and “Behavioral intention” with three items. Each
of the five constructs had Cronbach alpha coefficients ($\alpha$) as follows:
$\alpha=0.91$, $\alpha=0.9$, $\alpha=0.93$, $\alpha=0.83$, and $\alpha=0.87$
respectively. Based on these alpha coefficients, we adapted 12 items from
these constructs for our study-specific purpose.

We further conducted an exploratory factor analysis to categorize these items.
These included five items for measuring PSTs’ attitudes towards GenAI for
their learning, five items measuring PSTs’ attitudes towards GenAI for their
teaching, and two items measuring their general view about GenAI on ethics and
the possibility of incorporating it into their course of study. Table 1
provides some examples of the original items from Strzelecki (2023) and the
modified items for the attitude scale for this study(See Table I).

TABLE I: Examples of Modified Items Adapted for both Attitude and Use of GenAI
Applications

S/N | Original Item | Adapted Item
---|---|---
1 | Using ChatGPT increases your productivity in your studies | GenAI helped me understand various complex areas of my study.
2 | My interaction with ChatGPT is clear and understandable | GenAI explained things better to me than we had the time to do in class.
3 | I have the resources necessary to use ChatGPT | It is expensive using GenAI applications for studies/teaching
4 | I intend to continue using ChatGPT in the future | I suggest GenAI applications be incorporated into our courses and teaching practice
5 | I have the knowledge necessary to use ChatGPT | I didn’t/don’t require much assistance from colleagues for my teaching since I am using GenAI.
6 | Please choose your usage frequency for ChatGPT | How often do/did you use GenAI for your studies/teaching practice?

###  III-C Data Collection

We distributed the survey items through Google Forms for data collection. This
method was chosen for its convenience in gathering information from the range
of participants from the four teacher education institutions. In each of the
institutions, the participants were first oriented about the study, which
involved their consent, the voluntary nature of participation, and the purpose
of the study. We explained some of the categories in the context of this study
such as the use frequency GAI; “Very often” denoting at least Everyday Use of
AI in a Week, ‘Often’ (at most three days within a week), “Sometimes” (at most
two days a week), Rarely (Once a Week), Never= (Not at All). We then shared
the Google Forms survey link with them via WhatsApp, allowing them to respond
at their convenience. Eventually, we collected valid responses from all 167
participants for data analysis.

###  III-D Data Analysis

Before data analysis, we first exported all PSTs’ responses in Excel and then
checked for data completeness for further processing. We performed descriptive
statistics using frequency counts and percentages (%) to analyze the items
addressing the ways PSTs use GenAI as a learning buddy and as a teaching
assistant to answer Research Questions 1 and 2. To answer Research Question 3
(To assess PSTs attitudes towards GenAI applications), we converted the
numerical values of each Likert scale as Strongly Agree (SA) =5, Agree (A) =4,
Neutral (N) =3, Disagree (D) = 2, and Strongly Disagree (SD) = 1. We
established a binary of positive and negative categories mainly grounded on
the mean scores: a mean score above 3 indicated a positive attitude,
reflecting agreement or approval of the statement related to GenAI in teacher
education. Conversely, a mean score below 3 signifies a negative attitude,
indicating disagreement or skepticism towards the statement concerned with
GenAI’s in teacher education.

To answer research Question 4 on background factors of PSTs that predict their
attitudes towards GenAI, we conducted a regression analysis in R Statistical
Software (R version 4.3.3) with age, gender, and year in the study as the
predictors (See Table VII). The dependent variable, Attitude towards GenAI,
was derived from 12 Likert scale items. PSTs rated their attitudes on the
5-point Likert scale ranging from 1 (Strongly Disagree) to 5 (Strongly Agree).
We calculated a composite score for each participant by averaging their
responses across the 12 items:

$Composite\ Score_{i}=\frac{1}{n}\sum_{j=1}^{n}Q_{ij}$

where Composite Scorei is the composite score for the i-th participant,
$Q_{ij}$ is the response of the i-th participant to the j-th Likert item, and
n is the total number of Likert items. These composite scores were
standardized to have a mean of 0 and a standard deviation of 1.

Gender was coded as 0 for ‘Female’ and 1 for ‘Male,’ with 45 entries
identified as female and 122 as male. The variable Year of Study (Level) was
treated as categorical with three levels (‘200’, ‘300’, and ‘400’), and dummy
coding was applied with the lowest level (Level 200) as the reference
category. The multiple linear regression (lm()) model was specified as:

$\displaystyle\text{Attitude\\_GenAI}_{i}$
$\displaystyle=\beta_{0}+\beta_{1}\text{Age}_{i}+\beta_{2}\text{Gender}_{i}$
$\displaystyle\quad+\beta_{3}\text{Level\\_300}_{i}+\beta_{4}\text{Level\\_400}_{i}+\epsilon_{i}$
(1)

This regression equation models the attitude towards GenAI as a function of
age, gender, and year of study. The coefficients $\beta_{1}$, $\beta_{2}$,
$\beta_{3}$, and $\beta_{4}$ provide insights into how each of the factors
predicts PSTs’ attitude towards GenAI, while the intercept $\beta_{0}$ gives
the baseline level of PSTs’ attitude towards GenAI when all the predictors are
at their reference levels. The error term $\epsilon_{i}$ accounts for the
variability in attitudes, which may not be explained by the model.

We used the polr() function from the MASS library in R to do an ordered
logistic regression model to answer the second part of research question 4
about the background factors of PSTs that predict how often GenAI will be used
as a learning buddy or a teaching assistant. The dependent variable, GenAI use
frequency for both learning buddy and teaching assistant was measured on an
ordinal scale with five categories: ‘Never,’ ’Rarely,’ ‘Sometimes,’ ‘Often,’
and ‘Very often.’ The predictors included age (16-20, 21-25, 26-30, 31-35),
year of study (200, 300, 400), and gender (Male, Female). The ordered logistic
regression model was specified as follows:

$\log\left(\frac{P(Y\leq j)}{P(Y>j)}\right)=\alpha_{j}-\beta X-$
$(\beta_{1}\cdot\text{Age}+\beta_{2}\cdot\text{Year of Study}+$
$\beta_{3}\cdot\text{Gender})$

where $\alpha_{j}$ represents the thresholds between the categories. These
thresholds indicate the points on the latent variable scale where the
probability of transitioning from one category to the next changes.

After fitting the model, we extracted and summarized the coefficients for each
predictor, including their standard errors (SE), t-values, and p-values. This
allowed us to interpret the log odds of being in a higher category of GenAI
use for each unit change in the predictor variables and determine the
statistical significance of each predictor.

To enhance the interpretability of our findings, we created a data frame
containing all combinations of the predictor values and predicted the
probabilities of each GenAI use frequency category. For an ordered logistic
regression model with predictor variables, we calculated the predicted
probability as follows:

$P(Y=j)=P(Y\leq j)-P(Y\leq j-1)$

We then used the ggplot2 package in R (R version 4.3.3) to create a clear and
informative visualization of the predicted probabilities. This graphical
representation illustrated the distribution of the predicted probabilities
across the different background factors of age, year of study, and gender,
providing an understanding of each predictor on the frequency of GenAI use as
both learning buddy and teaching assistant.

##  IV Findings

###  IV-A Generative AI as a Learning buddy

This section presents the results on areas where PSTs use GenAI as a learning
buddy (See Table II) and how frequently they use it within a week (See Figure
1). The finding shows that PSTs mainly use GenAI in five different areas for
their learning. Ninety-three (38.11%) of PSTs specifically indicated that they
use GenAI to find reading materials for the courses of study, whereas 62 also
indicated they use GenAI to seek in-depth content explanations (25.41%).
Finding practical examples was another area in which the PSTs employed GenAI
to assist them in the learning of their course (37; 15.16%). About 25 (10.25%)
indicated using GenAI to extend their perspectives/knowledge in their courses
of study, while 27 (11.07%) indicated using GenAI assists them in conducting
reflections in their learning.

Additionally, Figure 1 explains the frequency at which PSTs use GenAI in these
areas of their learning. The findings show many of them “Sometimes’ (n = 70)
use GenAI as their learning buddy, whereas a substantial number of them
indicated they use it “Very often” (n = 50). The rest of the use spread within
“Often” (n = 25), “Never” (n = 20), and “Rarely” (n = 2). This shows a varying
reliance on GenAI for learning among the PSTs.

TABLE II: Uses of Generative AI by PSTs as a Learning Buddy Learning areas | # of teachers | %
---|---|---
Extending Perspectives/Knowledge | 25 | 10.25
Seeking In-depth Content Explanations | 62 | 25.41
Finding Practical Examples | 37 | 15.16
Finding Reading Materials | 93 | 38.11
Conducting Reflections | 27 | 11.07
Figure 1: Frequency of GenAI use as Learning Buddy

###  IV-B Generative AI as a Teaching Assistant

The finding in this section shows five instructional areas PSTs use GenAI as a
teaching assistant (see Table III). Specifically, the findings indicated that
ninety (33.33%) PSTs rely on GenAI to find teaching resources representing.
Fifty-eight (21.48%) of them indicated using GenAI in identifying assessment
strategies, whereas forty-four (16.30%) use GenAI for the generating of lesson
objectives and 43 (15.93%) indicated using GenAI to create their lesson plans
in general. A good number of the PSTs (35; 12.96%) showed using GenAI to find
sample lessons.

The bar chart in Figure 2 further illustrates the frequency of use of GenAI by
the PSTs as teaching assistant. The finding shows that most PSTs “Sometimes”
(n = 61) use GenAI for teaching whiles thirty-five (n = 32) of them had
“Never” use GenAI in their teaching. A good number of them, “Often” (n = 31),
use GenAI for teaching with some using GenAI “Very often” (n = 25). About
eighteen (n = 18) of the PSTs “Rarely” use GenAI for teaching. This
distribution suggests that while some PSTs are frequent users of GenAI for
teaching, others are less inclined to use it in their teaching activities.
This shows a varying reliance on GenAI for teaching among the PSTs.

TABLE III: Uses of Generative AI by PSTs as a Teaching Assistant Instructional area | # of teachers | %
---|---|---
Identifying Assessment Strategies | 58 | 21.48
Identifying Lesson Objectives | 44 | 16.30
Creating Lesson Plans | 43 | 15.93
Finding Sample of Lessons | 35 | 12.96
Finding Teaching Resources | 90 | 33.33
Figure 2: Frequency of GenAI Use as Teaching Assistant

###  IV-C Exploratory Factor Analysis of PSTs’ Attitudes

Exploratory Factor Analysis (EFA) of the items measuring attitude toward GenAI
was performed (see Table IV). This was performed on the 12 attitude items
using the minimum residual (minres) method with a varimax rotation. The
analysis involved a three-factor solution explaining 54% of the variance. The
factors were labeled attitude towards using GenAI for learning (GenAIL),
attitude towards using GenAI for teaching (GenAIT), and Ethical and Advocacy
(EAA), with SS loadings of 3.06, 2.01, and 1.38, respectively, indicating the
amount of variance captured by each factor. The mean item complexity was 1.3,
suggesting that items tend to load on more than one factor. The root mean
square of the residuals (RMSR= 0.05) indicated a good fit, with a Tucker Lewis
Index (TLI=0.899) and an RMSEA of 0.074, suggesting an acceptable fit to the
data. The Bayesian Information Criterion (BIC) was -91.96, further supporting
the model fit.

TABLE IV: Factor Loadings from Exploratory Factor Analysis

S/N | Item | GenAIT | GenAIL | EAA | (h^2) | (u^2) | Complexity
---|---|---|---|---|---|---|---
1 | GenAI has the potential to positively improve my content knowledge. | .10 | .52 | -.20 | .33 | .673 | 1.4
2 | I don’t require much assistance from colleagues for my studies since I am using GenAI | .22 | .81 | -.04 | .70 | .300 | 1.2
3 | GenAI helped me understand various complex areas of my study | .20 | .67 | .22 | .53 | .465 | 1.4
4 | With the help of GenAI, I can get more reading materials | .27 | .62 | .20 | .50 | .505 | 1.6
5 | Reduced need for colleague assistance in teaching with GenAI | .78 | .17 | -.08 | .65 | .355 | 1.1
6 | I don’t require much assistance from colleagues for my teaching since I am using GenAI | .94 | .02 | -.10 | .89 | .107 | 1.0
7 | GenAI has the potential to improve my pedagogical skills (teaching skills) of teachers | .66 | .26 | .03 | .50 | .495 | 1.3
8 | I felt more confident preparing and teaching with GenAI’s assistance | .73 | -.01 | .22 | .59 | .413 | 1.2
9 | It is expensive using GenAI applications for studies/teaching | -.14 | .10 | .98 | 1.00 | .001 | 1.1
10 | There is inaccuracy and distrust in GenAI applications’ information | .21 | -.09 | .42 | .23 | .772 | 1.5
11 | I suggest GenAI applications be incorporated into our courses and teaching practice | .59 | .20 | .18 | .42 | .577 | 1.4
12 | Student might cheat and lack critical/creative skills development due to GenAI | -.08 | .33 | -.05 | .12 | .882 | 1.2

###  IV-D Attitudes of PSTs towards GenAI

To answer RQ3 on PSTs’ attitude toward GenAI in their learning and teaching,
the exploratory factor analysis implies three factors for teachers’ attitudes
toward GenAI, including the use of GenAI in teaching (GenAIT), in learning
(GenAIL), and Ethical and Advocacy Factor (EAA), as shown in Table V. Items
related to GenAIL such as Item1 (Factor Loading ($\lambda$=.52), Item 2
($\lambda$=.81), Item3 ($\lambda$=.67), Item4 ($\lambda$=.62), and Item 12
($\lambda$=.33) exhibit strong factor loadings, suggesting PSTs’ attitudes
towards GenAI in their learning, with Item 6 ($\lambda$=.94) being
particularly indicative of GenAIL. In the GenAIT factor, Item 5 ($\lambda$=
.78), Item 6 ($\lambda$=.94), Item 7 ($\lambda$=.66), Item 8 ($\lambda$=.73),
and Item11 ($\lambda$=.59) underscore PSTs’ attitudes about GenAI’ roles in
enhancing their teaching, emerging as a key factor shaping their attitudes
toward GenAIT. Comprising Item 9 ($\lambda$=.98) and Item 10 ($\lambda$=.42),
the EAA factor captures attitudes towards ethical and advocacy for GenAI
integration into both their use of GenAI for learning and teaching.

The overall mean and standard deviation (SD) for the constructs GenAIL,
GenAIT, and EAA were analyzed to determine PSTs’ overall attitudes towards
GenAI. The results indicated that PSTs have a moderately positive attitude
towards GenAI for learning (M=3.678, SD=0.786), accompanied by relatively low
variability in responses. Similarly, the results revealed that PSTs have a
moderately positive attitude towards GenAI for teaching (M=3.458, SD=0.950),
with slightly higher variability in responses compared to GenAIL.
Additionally, regarding the construct EAA, the results indicated PSTs’ neutral
to slightly positive attitude towards GenAI (M=3.005, SD=0.815), accompanied
by relatively low variability in responses.

Among other findings, the PSTs positive attitude towards GenAIL was
specifically based on their belief that GenAI assists them in “accessing more
reading materials with GenAI” (M = 4.01, SD = 0.93) while helping them to
understand complex areas of study (M = 3.72, SD = 0.53). Furthermore, PSTs’
positive attitude towards Generative AI for Teaching (GenAIT) was based on
several reasons. They specifically agreed that GenAI reduces the need for
colleague assistance in teaching (M = 3.42, SD = 1.16) and that they do not
require much assistance from colleagues for their teaching since using GenAI
(M = 3.34, SD = 1.16). In terms of Ethical and Advocacy of GenAI (EAA), they
expressed a relatively neutral but slightly positive attitude towards the cost
of using GenAI applications for learning and teaching (M = 3.21, SD = 0.82).
However, there was some concern regarding the accuracy and trustworthiness of
the information provided by GenAI applications, with PSTs showing a more
neutral attitude in this regard (M = 2.80, SD = 0.81).

TABLE V: Pre-service Teachers’ Attitude Measurement and Factor Loadings

Construct |  Overall Mean |  Overall SD |  Item |  GenAIT ($\lambda$) |  GenAIL ($\lambda$) |  EAA ($\lambda$) |  Description
---|---|---|---|---|---|---|---
GenAIL |  3.678 |  0.786 |  1 |  .10 |  .52 |  -.20 |  GenAI has the potential to positively improve my content knowledge. Items show strong loadings on learning factor, highlighting GenAI’s ability to assist PSTs in the learning of their courses. Item 2 with highest loading
|  |  |  2 |  .22 |  .81 |  -.04 |  I don’t require much assistance from colleagues for my studies since I am using GenAI
|  |  |  3 |  .20 |  .67 |  .22 |  GenAI helped me understand various complex areas of my study
|  |  |  4 |  .27 |  .62 |  .20 |  With the help of GenAI, I can get more reading materials
|  |  |  12 |  -.08 |  .33 |  -.05 |  Student might cheat and lack critical/creative skills development due to GenAI
GenAIT |  3.458 |  0.950 |  5 |  .78 |  .17 |  -.08 |  Reduced need for colleague assistance in teaching with GenAI. Items show strong loading that GenAI’s can assist PSTs in their teaching. Highest loading with Item 6 signifies key dimension towards teaching.
|  |  |  6 |  .94 |  .02 |  -.10 |  I don’t require much assistance from colleagues for my teaching since I am using GenAI
|  |  |  7 |  .66 |  .26 |  .03 |  GenAI has the potential to improve my pedagogical skills (teaching skills) of teachers
|  |  |  8 |  .73 |  -.01 |  .22 |  I felt more confident preparing and teaching with GenAI’s assistance
|  |  |  11 |  .59 |  .20 |  .18 |  I suggest GenAI applications be incorporated into our courses and teaching practice
EAA |  3.005 |  0.815 |  9 |  -.14 |  .10 |  .98 |  It is expensive using GenAI applications for studies/teaching. Captures PSTs attitude towards GenAI on ethical issues and advocacy for inclusion in their program of study. Item 9 with highest loading
|  |  |  10 |  .21 |  -.09 |  .42 |  There is inaccuracy and distrust in GenAI applications’ information

###  IV-E Factors Predicting PSTs’ Attitude and GenAI Use

To answer RQ4, we tested several hypotheses (H). These hypotheses helped us
determine which PSTs’ background factor predicts their attitudes toward GenAI
(see Table VI) as well as their use of GenAI as their learning buddy and
teaching assistant (see Table VII). Specifically, we focus on the background
factors of age, year of study, and gender.

####  IV-E1 Background Factors Predicting Attitudes toward GenAI (BFAG)

  * •

H0BFAG: The background factors of PSTs, such as age, year of study, and
gender, do not significantly predict their attitudes toward GenAI.

  * •

H1BFAG: One or more background factors of PSTs, such as age, year of study,
and gender, significantly predict their attitudes toward GenAI.

The results showed that none of the background factors (gender, year of study,
and age) significantly predicted PSTs’ attitudes toward GenAI (See Table VI).
Specifically, Male was not a significant predictor (B = 0.04, SE = 0.09, p =
.683). Similarly, among the year of study, both Level 300 (B = -0.06, SE =
0.17, p = .717) and Level 400 (B = -0.06, SE = 0.17, p = .722) did not
significantly predict PSTs’ attitudes toward GenAI. Age also did not show
significant predictions, with the age groups 21-25 (B = -0.32, SE = 0.26, p =
.233), 26-30 (B = -0.22, SE = 0.27, p = .410), and 31-35 (B = -0.22, SE =
0.33, p = .520) all yielding non-significant results. Based on the non-
significant p-values for each predictor variable, we fail to reject the null
hypothesis for each background factor.

TABLE VI: Regression Analysis for Attitude Towards Generative AI by Gender, Level, and Age Predictor | B | SE | t(df) | p
---|---|---|---|---
Gender |  |  |  |
Intercept | 3.50 | .08 | 45.20 | <.001
Male | .04 | .09 | 0.41 | .683
Year of Study |  |  |  |
Intercept | 3.58 | .16 | 22.81 | <.001
Level 300 | -.06 | .17 | -0.36 | .717
Level 400 | -.06 | .17 | -0.36 | .722
Age |  |  |  |
Intercept | 3.80 | .26 | 14.65 | <.001
Age 21-25 | -.32 | .26 | -1.20 | .233
Age 26-30 | -.22 | .27 | -0.83 | .410
Age 31-35 | -.22 | .33 | -0.65 | .520

####  IV-E2 Background Factors Predicting GenAI Use as a Learning Buddy and as
a Teaching Assistant (BFUG)

  * •

H0BFUA: The background factors of PSTs, such as age, year of study, and
gender, do not significantly predict the ordered categories of their use of
GenAI for learning and teaching.

  * •

H1BFUG: One or more background factors of PSTs, such as age, year of study,
and gender, significantly predict the ordered categories of their use of GenAI
for learning and teaching.

Table VII shows the ordinal logistic regression analysis for GenAI as a
learning buddy (GenAIL) and GenAI as a teaching assistant (GenAIT) models to
understand the background factors that can predict the frequency of GenAI use
among PSTs. The ordered logistic regression revealed significant predictors
for the use of GenAI for learning, with age 31-35 (B = 3.1267, SE = 1.2425, p
= .0119) and year of study 300 (B = 1.6054, SE = 0.6513, p = .0137) being
significant, while other factors were not. Similarly, significant predictors
for the use of GenAI for teaching included year of study 300 (B = 2.121, SE =
0.6824, p = .0019) and year of study 400 (B = 1.674, SE = 0.6816, p = .0140),
with age 31-35 approaching significance (B = 2.226, SE = 1.2601, p = .0773).
Gender and other age groups did not significantly predict the use of GenAI as
either a learning buddy or a teaching assistant. Based on the results from
Table VII, we reject the null hypothesis for the significant predictors and
fail to reject it for the non-significant predictors. Therefore, we conclude
that specific background factors, particularly certain age groups and years of
study, significantly predict the use of GenAI for learning and teaching, while
gender does not.

TABLE VII: Ordinal Logistic Regression Coefficients, Thresholds, and Significance for Frequency of GenAI Use (GenAIL and GenAIT) Parameter |  GenAIL Coefficient ($\beta$) |  GenAIL Std. Error (SE) |  GenAIL t Value |  GenAIL p Value |  GenAIT Coefficient ($\beta$) |  GenAIT Std. Error (SE) |  GenAIT t Value |  GenAIT p Value
---|---|---|---|---|---|---|---|---
Predictors |  |  |  |  |  |  |  |
Age 21-25 |  1.5331 |  0.9968 |  1.538 |  0.1240 |  0.727 |  0.9790 |  0.742 |  0.4580
Age 26-30 |  1.5022 |  1.0385 |  1.447 |  0.1480 |  0.794 |  1.0161 |  0.782 |  0.4343
Age 31-35 |  3.1267* |  1.2425 |  2.517* |  0.0119* |  2.226 |  1.2601 |  1.766 |  0.0773
Year of Study 300 |  1.6054* |  0.6513 |  2.465* |  0.0137* |  2.121* |  0.6824 |  3.109* |  0.0019*
Year of Study 400 |  0.7089 |  0.6481 |  1.094 |  0.2740 |  1.674* |  0.6816 |  2.456* |  0.0140*
Gender Female |  -0.4720 |  0.3515 |  -1.343 |  0.1794 |  -0.444 |  0.3507 |  -1.267 |  0.2052
Thresholds |  |  |  |  |  |  |  |
Never — Rarely |  -0.0398 |  1.1936 |  -0.033 |  0.9734 |  0.863 |  1.1897 |  0.726 |  0.4681
Rarely — Sometimes |  0.3527 |  1.1890 |  0.297 |  0.7667 |  1.503 |  1.1942 |  1.259 |  0.2081
Sometimes — Often |  2.6754* |  1.2077 |  2.215* |  0.0267* |  3.144* |  1.2115 |  2.595* |  0.0094*
Often — Very Often |  3.4411* |  1.2164 |  2.829* |  0.0047* |  4.250* |  1.2248 |  3.470* |  0.0005*

####  IV-E3 Predicted Probabilities of GenAI Use

The predicted probabilities assisted us in determining the specific background
factors that predict GenAI use as a learning buddy (GenAIL) and as a teaching
assistant (GenAIT). The results indicate that age and year of study
significantly predict the frequency of GenAI use among the PSTs, with older
PSTs and those in their later years of study being more likely to use GenAI
frequently for both learning and teaching.

Figure 3 displays the predicted probabilities of GenAI use frequency for
learning across different age groups, levels of study, and gender among PSTs.
For males aged 31-35 in their third year of study, there is a high probability
(78.43%) of using GenAI “Very Often” for learning, compared to a lower
probability for other age groups and levels of study. For females aged 31–35
in their third year of study, the probability of using GenAI “Very Often” for
learning is also high (69.40%). Younger age groups (21–25 and 26–30) and those
in their second or fourth year of study have higher probabilities of using
GenAI “Sometimes” (52.31%) or “Rarely” (17.18% for 21–25, second year, male).

Figure 4 illustrates the predicted probabilities of GenAI use frequency for
teaching. For males aged 31–35 in their third and fourth years of study, there
is a high probability of using GenAI “Very Often” for teaching (59.74% for
fourth year). For females aged 31–35 in their third year of study, there is a
high probability of using GenAI “Very Often” for teaching (48.06%). Younger
age groups and those in their second year of study have higher probabilities
of using GenAI “Never” (24.96% for 21–25, second year, female) or “Rarely.”
These findings highlight the significant influence of age and year of study on
the frequency of GenAI use for both learning and teaching, with older students
and those further along in their studies showing higher probabilities of
frequent GenAI use. Gender, however, does not appear to significantly
influence the frequency of GenAI use in either context.

Figure 3: Predicted Probability of GenAI Use as a Learning Buddy Figure 4:
Predicted Probability of GenAI Use as a Teaching Assistant

##  V Discussion

Research has consistently revealed that PSTs face difficulties in integrating
theoretical knowledge from their coursework with their clinical or field
practice [39]. Although this study did not specifically address the connection
between theory and practice, it highlighted the potential of GenAI to bridge
this gap within teacher education programs. From the findings of this study,
GenAI can support PSTs in their learning across various aspects of their
coursework, which signifies GenAI’s substantial contribution to PSTs’
theoretical knowledge [13]. Additionally, the findings indicate GenAI has the
capacity to assist PSTs across different aspects of their teaching practice,
indicating its potential to enhance practice. The different areas of learning
and teaching that GenAI could possibly support PSTs in teacher education
programs are discussed subsequently.

More specifically on the areas of teaching, the findings prove that GenAI can
support PSTs in finding practical examples that illustrate concepts for their
lessons. This finding is congruent with the assertion by Rahman et al. [20]
that GenAI can provide a wide array of real-world scenarios and case studies
with relevant and relatable examples for teaching. This implies that GenAI has
the potential to perform mentor-related and instructor-related roles such as
modeling or demonstrating practical lessons for PSTs during their teaching
practices [40]. This makes GenAI literacy crucial not only for PSTs but for
the triad in teacher education programs [41].

One of the key challenges of teacher education programs is inadequate
resources for their teaching practices [42]. Supporting other studies, the
finding of this study explicitly indicates that GenAI can significantly assist
PSTs in finding a wide range of teaching resources for their classroom
teaching [23, 2]. This signifies that GenAI can improve resource
accessibility, teaching quality, and professional development, ultimately
preparing PSTs to be effective and innovative educators. Therefore,
integrating GenAI into teacher education and empowering PSTs’ effective use
can address the longstanding challenges of resource deficits.

Whalen and Mouza [12] asserted that PSTs can leverage GenAI to explore a
multitude of cultural contexts, enriching their understanding and broadening
their intellectual horizons. Likewise, this study specifically reveals that
GenAI can expand content knowledge, perspectives, and in-depth content
explanations. This explains the claim by AlAli and Wardat [11] that GenAI
employs vast datasets to provide access to diverse viewpoints and various
educational paradigms beyond traditional contexts. This literally further
suggests that, with GenAI, PSTs can access a wider range of educational
content and viewpoints to promote culturally relevant education.

Reflective practice is crucial for all teachers, whether PSTs or in-service
teachers, and research has proven the significance of having peer support
enhance reflective practice [43]. The finding on GenAI capability to support
PSTs in reflective practice shows it can serve as a peer-support in this
exercise of PSTs. This finding adds up to the findings of recent studies that
GenAI has the potential to provide feedback on reflective writings and help
teachers develop critical self-awareness [26, 27].

Moreover, the findings of this study show that PSTs rely on GenAI for
assessment. This is noteworthy as traditional assessment methods often lack
the flexibility and adaptability needed to address diverse student needs and
learning styles [44]. GenAI can analyze extensive educational data to
recommend varied assessment techniques such as formative assessments,
performance-based tasks, and adaptive testing [30]. The use of GenAI by PSTs
may ensure a more holistic assessment of students’ progress and decision-
making [45].

The frequency with which PSTs employ GenAI for the above use areas, learning
and teaching, is intriguing, indicating how GenAI is becoming an integral
component of PSTs’ education, despite not being officially introduced [46].
This highlights the need for teacher education programs to consider
effectively integrating GenAI, particularly as PSTs continue to navigate
ethical challenges and inaccuracies associated with GenAI applications [47]. A
well-structured program on GenAI in teacher education could address some of
these issues.

While PSTs expressed significant use of GenAI, the findings show background
factors such as age and year of study could predict the frequency of GenAI
use, supporting the notion that older teachers tend to appreciate educational
technology more than younger ones [48]. However, it also showed that PSTs in
the early years of their programs use GenAI more for coursework, while those
in later years use it more for practical experiences. This could be a result
of PSTs in their early years of teacher education program exposing PSTs to
coursework in their early years of their study than in the later years where
they usually engage in teaching practices and internships. This provides a
picture of how a proposed GenAI curriculum should be designed to match the
timelines and needs of PSTs in teacher education. The GenAI curriculum should
largely focus more on coursework than practical teaching aspects. The finding
that gender does not significantly influence GenAI use for both learning and
teaching is noteworthy, as it supports the literature suggesting that
technology use is becoming gender-neutral as both males and females see
themselves as competent in technology use [48]. However, this finding
contrasts with Chidiac et al. [49] finding, which indicated that males use
technology less than females. In brief, the findings of the background factors
suggest that while age can significantly influence the use of GenAI, gender
differences in the frequency of GenAI use may vary across different contexts
and populations.

Attitude is a key component essential for technology acceptance, and a
negative or positive attitude may have a significant impact on the growth of
any emerging technology such as GenAI [50]. Interestingly, the findings of
this study imply that the perceived benefits of GenAI surpass various
demographic differences, and therefore, the PSTs’ attitudes towards GenAI were
not predicted by any background factors, indicating a generally positive
disposition towards GenAI regardless of their diverse backgrounds. This favors
the statement that PSTs generally have a positive attitude towards GenAI in
research [8]. This suggests that teacher education programs can integrate
GenAI without concern for demographic barriers. This universal acceptance
facilitates the smoother implementation and wider adoption of GenAI
applications in teacher training.

Pre-service teachers’ voice is essential in the triad of teacher preparation
programs [51, 52]. There have been numerous studies that indicate PSTs
expressing positive attitudes towards GenAI and have been calling for the
integration of GenAI in the teacher preparation programs [53, 3, 46, 8].
Despite the challenges of GenAI as reported by PSTs as cost-effective and
inaccuracies, they are still advocating for the integration of GenAI into
their programs, highlighting their needs and further demonstrating the
importance of addressing these needs within teacher education programs [51].
This crucial advocacy supports many researchers who have called for the
proposal of well-defined guidelines and policies of GenAI use by PSTs to
ensure responsible and effective integration of GenAI in teacher education
[28, 29].

##  VI Conclusion

In this study, we aimed to find out how PSTs use GenAI applications for their
coursework and teaching practices, as well as their attitude toward GenAI. The
study addressed four key questions to find out the ways PSTs use GenAI as a
learning buddy during their coursework, how they employ GenAI as a teaching
assistant during their teaching practice, the attitude of PSTs towards GenAI
applications, and the background factors of PSTs’ that predict their attitudes
toward GenAI and the uses of GenAI for learning and teaching.

The main findings show that PSTs frequently use GenAI to access reading
materials, look for content explanations, and find useful examples for both
their coursework and teaching practices. Age and year of study significantly
predict the frequency of GenAI use, whereas gender does not. Despite these
variations, PSTs generally expressed a positive yet cautious attitude toward
GenAI applications and showed concerns about the cost of access, the accuracy,
and the reliability of the information that GenAI provided.

Even though the findings conclude that specific background factors,
particularly age and years of study, significantly predict the use of GenAI as
a learning buddy and a teaching assistant among PSTs, these factors do not
significantly affect their attitudes toward GenAI application. It specifies
that older PSTs and those further along in their teacher education programs
may use GenAI more frequently, but their attitude towards the GenAI
applications remains unchanged. Given concerns about the accuracy and
trustworthiness of GenAI applications, it is essential to address these issues
to ensure PSTs can confidently rely on GenAI in their teacher preparation
programs. We recommend targeted strategies to integrate GenAI more effectively
into both the learning and teaching processes for PSTs.

##  VII Limitations

Though the use of convenience sampling, a non-probabilistic sampling method,
was extremely useful in this exploratory study, we acknowledge that it is not
representative of the larger population of PSTs in the four institutions in
Ghana. Hence, further research might need to be careful when generalizing the
findings to a larger population. We, therefore, suggest that future studies
may involve probabilistic sampling to enhance the representativeness of the
results. Additionally, it would be beneficial for subsequent research to
explore deeper into the varying uses of GenAI by the PSTs in both their
coursework and teaching practices.

Moreover, we acknowledge that the PSTs in this study may have different usage
needs. Notably, the PSTs in their third and final year are more engaged in
actual teaching practice compared to the level 200 PSTs. Additionally, the
study was conducted during a period when there were no first-year students on
campus due to admission transitions. This context likely influenced the
findings and may account for some of the observed differences in technology
use and attitude.

## References

  * [1] G. G. Lee, L. Shi, E. Latif, Y. Gao, A. Bewersdorf, M. Nyaaba, and X. Zhai, “Multimodality of ai for education: Towards artificial general intelligence,” _arXiv preprint arXiv:2312.06037_ , 2023.
  * [2] G. G. Lee and X. Zhai, “Using chatgpt for science learning: A study on pre-service teachers’ lesson planning,” 2024, https://doi.org/10.13140/RG.2.2.13711.76965.
  * [3] M. Nyaaba and X. Zhai, “Generative ai professional development needs for teacher educators,” _Journal of AI_ , vol. 8, no. 1, pp. 1–13, 2024.
  * [4] S. Biswas, “Role of chat gpt in education,” 2023, available at SSRN 4369981.
  * [5] S. Guo, Y. Zheng, and X. Zhai, “Artificial intelligence in education research during 2013–2023: A review based on bibliometric analysis,” _Education and Information Technologies_ , pp. 1–23, 2024.
  * [6] T. Adiguzel, M. H. Kaya, and F. K. Cansu, “Revolutionizing education with ai: Exploring the transformative potential of chatgpt,” _Contemporary Educational Technology_ , vol. 15, no. 3, p. ep429, 2023.
  * [7] M. Neumann, M. Rauschenberger, and E.-M. Schön, ““we need to talk about chatgpt”: The future of ai and higher education,” 2023.
  * [8] H. C. Sarica, “Pre-service teachers’ perspectives: Exploring the integration of artificial intelligence applications in education,” in _INTED2024 Proceedings_ , 2024.
  * [9] G.-G. Lee, S. Mun, M.-K. Shin, and X. Zhai, “Collaborative learning with artificial intelligence speakers,” _Science & Education_, pp. 1–29, 2024\.
  * [10] E. Latif, R. Parasuraman, and X. Zhai, “Physicsassistant: An llm-powered interactive learning robot for physics lab investigations,” _arXiv preprint arXiv:2403.18721_ , 2024.
  * [11] R. AlAli and Y. Wardat, “How chatgpt will shape the teaching learning landscape in future,” _Journal of Educational and Social Research_ , vol. 14, no. 2, pp. 336–345, 2024.
  * [12] J. Whalen and C. Mouza, “Chatgpt: Challenges, opportunities, and implications for teacher education,” _Contemporary Issues in Technology and Teacher Education_ , vol. 23, no. 1, pp. 1–23, 2023.
  * [13] W. M. Lim, A. Gunasekara, J. L. Pallant, J. I. Pallant, and E. Pechenkina, “Generative ai and the future of education: Ragnarök or reformation? a paradoxical perspective from management educators,” _The International Journal of Management Education_ , vol. 21, no. 2, p. 100790, 2023.
  * [14] C. A. Issaka, M. Nyaaba, and F. A. Iddrisu, “Attitudes and concerns of pre-service teachers’ about inclusive education enshrined in the new four-year basic education curriculum in ghana,” _European Journal of Education and Pedagogy_ , vol. 3, no. 1, pp. 89–94, 2022.
  * [15] J. Share, T. Mamikonyan, and E. Lopez, “Critical media literacy in teacher education, theory, and practice,” in _Oxford research encyclopedia of education_ , 2019.
  * [16] T. van der Vorst and N. Jelicic, “Artificial intelligence in education: Can ai bring the full potential of personalized learning to education?” 2019.
  * [17] A.-C. E. Ding, L. Shi, H. Yang, and I. Choi, “Enhancing teacher ai literacy and integration through different types of cases in teacher professional development,” _Computers and Education Open_ , vol. 6, p. 100178, 2024.
  * [18] L. Sheridan, “Examining changes in pre-service teachers’ beliefs of pedagogy,” _Australian Journal of Teacher Education (Online)_ , vol. 41, no. 3, pp. 1–20, 2016.
  * [19] A. Bewersdorff, C. Hartmann, M. Hornberger, K. Seßler, M. Bannert, E. Kasneci, G. Kasneci, X. Zhai, and C. Nerdel, “Taking the next step with generative artificial intelligence: The transformative role of multimodal large language models in science education,” 2024, https://doi.org/10.48550/arXiv.2401.00832.
  * [20] M. M. Rahman, H. J. Terano, M. N. Rahman, A. Salamzadeh, and M. S. Rahaman, “Chatgpt and academic research: a review and recommendations based on practical examples,” _Journal of Education, Management and Development Studies_ , vol. 3, no. 1, pp. 1–12, 2023.
  * [21] K. Stutchbury, M. Ebubedike, S. Amos, and L. Chamberlain, “Professional development in the digital age: supporting improvements in teacher education through moocs,” _Open Learning: The Journal of Open, Distance and e-Learning_ , pp. 1–24, 2023, https://doi.org/10.1080/02680513.2023.2195875.
  * [22] J. Sun, H. Ma, Y. Zeng, D. Han, and Y. Jin, “Promoting the ai teaching competency of k-12 computer science teachers: A tpack-based professional development approach,” _Education and Information Technologies_ , vol. 28, no. 2, pp. 1509–1533, 2023.
  * [23] O. Koraishi, “Teaching english in the age of ai: Embracing chatgpt to optimize efl materials and assessment,” _Language Education and Technology_ , vol. 3, no. 1, 2023.
  * [24] L.-H. Hsia, G.-J. Hwang, and J.-P. Hwang, “Ai-facilitated reflective practice in physical education: an auto-assessment and feedback approach,” _Interactive Learning Environments_ , pp. 1–20, 2023.
  * [25] W. Dias, “Reflective practice, artificial intelligence, and engineering design: Common trends and interrelationships,” _AI EDAM_ , vol. 16, no. 4, pp. 261–271, 2002.
  * [26] A. Papola-Ellis, “Teacher candidates’ use of critical literacy to shift thinking about texts and social justice,” _Reading Horizons: A Journal of Literacy and Language Arts_ , vol. 59, no. 2, p. 4, 2020.
  * [27] G. van den Berg and E. du Plessis, “Chatgpt and generative ai: Possibilities for its contribution to lesson planning, critical thinking and openness in teacher education,” _Education Sciences_ , vol. 13, no. 10, p. 998, 2023\.
  * [28] A. Holland and C. Ciachir, “A qualitative study of students’ lived experience and perceptions of using chatgpt: immediacy, equity and integrity,” _Interactive Learning Environments_ , pp. 1–12, 2024.
  * [29] T. Rasul, S. Nair, D. Kalendra, M. Robin, F. de Oliveira Santini, W. J. Ladeira, M. Sun, I. Day, R. A. Rather, and L. Heathcote, “The role of chatgpt in higher education: Benefits, challenges, and future research directions,” _Journal of Applied Learning and Teaching_ , vol. 6, no. 1, 2023\.
  * [30] X. Zhai, “Practices and theories: How can machine learning assist in innovative assessment practices in science education,” _Journal of Science Education and Technology_ , vol. 30, no. 2, pp. 139–149, 2021.
  * [31] X. Zhai, M. Nyaaba, and W. Ma, “Can generative ai and chatgpt outperform humans on cognitive-demanding problem-solving tasks in science?” _Science & Education_, pp. 1–22, 2024, https://doi.org/10.1007/s11191-024-00496-1.
  * [32] X. Zhai, “Chatgpt and ai: The game changer for education,” 2023, available at SSRN.
  * [33] C. Cao, Z. Ding, G.-G. Lee, J. Jiao, J. Lin, and X. Zhai, “Elucidating stem concepts through generative ai: A multi-modal exploration of analogical reasoning,” _arXiv preprint arXiv:2308.10454_ , 2023.
  * [34] M. Sakamoto, S. TAN, and S. CLIVAZ, “Social, cultural and political perspectives of generative ai in teacher education: Lesson planning in japanese teacher education,” in _Exploring New Horizons: Generative Artificial Intelligence and Teacher Education_ , 2024, p. 178.
  * [35] S. C. Lee and K. Kang, “The effect of ‘integrated science logic & essay’class using generative ai on technological pedagogical and content knowledge (tpack) of pre-service science teachers,” _Journal of Curriculum Evaluation_ , vol. 27, no. 1, pp. 133–155, 2024.
  * [36] E. Latif, G. Mai, M. Nyaaba, X. Wu, N. Liu, G. Lu, S. Li, T. Liu, and X. Zhai, “Agi: Artificial general intelligence for education,” _arXiv preprint arXiv:2304.12479_ , 2023.
  * [37] C. Foster, “Methodological pragmatism in educational research: From qualitative-quantitative to exploratory-confirmatory distinctions,” _International Journal of Research & Method in Education_, vol. 47, no. 1, pp. 4–19, 2024.
  * [38] J. Kittur, “Conducting quantitative research study: A step-by-step process,” _Journal of Engineering Education Transformations_ , vol. 36, no. 4, pp. 100–112, 2023.
  * [39] O. L. Ferraz, C. Vidoni, and M. V. Boas, “Bridging the gap between theory and practice: the impact of school–university partnership in a pete program,” _Sport, Education and Society_ , vol. 26, no. 7, pp. 788–799, 2021.
  * [40] A. Ambrosetti, “Are you ready to be a mentor?: Preparing teachers for mentoring pre-service teachers,” _Australian Journal of Teacher Education (Online)_ , vol. 39, no. 6, pp. 30–42, 2014.
  * [41] Z. Melek and D. Schmidt-Crawford, “Genai in teacher education: Teacher educators’ views on ai in pre-service teacher education,” in _Society for Information Technology & Teacher Education International Conference_, 2024\.
  * [42] M. K. Barbour, “Models and resources for online teacher preparation and mentoring,” in _Lessons learned in teacher mentoring: Supporting educators in K-12 online learning environments_ , 2012, pp. 83–102.
  * [43] B. Milliard, “Utilization and impact of peer-support programs on police officers’ mental health,” _Frontiers in Psychology_ , vol. 11, p. 562545, 2020.
  * [44] X. Zhai and E. Wiebe, “Technology-based innovative assessment,” in _Classroom-based STEM assessment_ , 2023, pp. 99–125.
  * [45] A. Khademi, “Can chatgpt and bard generate aligned assessment items? a reliability analysis against human performance,” _arXiv preprint arXiv:2304.05372_ , 2023.
  * [46] I. T. Sanusi, M. A. Ayanwale, and A. E. Tolorunleke, “Investigating pre-service teachers’ artificial intelligence perception from the perspective of planned behavior theory,” _Computers and Education: Artificial Intelligence_ , vol. 6, p. 100202, 2024.
  * [47] V. V. Ravi Kumar and R. Raman, “Student perceptions on artificial intelligence (ai) in higher education,” 2022.
  * [48] A. Antonio, R. Probitchado Jr, C. Ricohermoso, A. Saavedra, and J. M. de la Rama, “Gender differences in technological competence among science teachers: Implications,” 2020.
  * [49] M. Chidiac, C. Ross, H. R. Marston, and S. Freeman, “Age and gender perspectives on social media and technology practices during the covid-19 pandemic,” _International Journal of Environmental Research and Public Health_ , vol. 19, no. 21, p. 13969, 2022, https://doi.org/10.3390/ijerph192113969.
  * [50] N. Iqbal, H. Ahmed, and K. A. Azhar, “Exploring teachers’ attitudes towards using chatgpt,” _Glob. J. Manag. Adm. Sci_ , vol. 3, pp. 97–111, 2022.
  * [51] Cecilia and W. Hu, “Students’ voices on generative ai: Perceptions, benefits, and challenges in higher education,” _arXiv pre-print server_ , 2023, https://doi.org/None, arxiv:2305.00290.
  * [52] S. P. Nahal, “Voices from the field: Perspectives of first-year teachers on the disconnect between teacher preparation programs and the realities of the classroom,” _Research in Higher Education Journal_ , vol. 8, p. 1, 2010.
  * [53] M. A. Ayanwale, O. P. Adelana, R. R. Molefi, O. Adeeko, and A. M. Ishola, “Examining artificial intelligence literacy among pre-service teachers for future classrooms,” _Computers and Education Open_ , vol. 6, p. 100179, 2024\.