Oluwagbenga, T. T., & Itanyi, M. F. (2026). The Impact of Artificial Intelligence on Students' Problem Identification and Convergent Thinking Abilities. International Journal of Research, 13(1), 438–452. https://doi.org/10.26643/ijr/2026/18
1Taiwo Timothy
Oluwagbenga , 2Mamudu Francis Itanyi
1,2Thomas Adewumi University, Oko,
Kwara State, Nigeria.
timothy.taiwo@tau.edu.ng
Abstract
The rapid integration of
generative artificial intelligence (AI) tools, such as ChatGPT and Gemini, into
higher education has sparked considerable debate regarding their influence on
students' cognitive abilities, particularly problem identification and convergent
thinking. This study examines the impact of AI usage on these skills among
full-time undergraduate students in accredited public and private universities
in Kwara State, Nigeria. Drawing on a target population of approximately 90,000
students, a sample of 385 participants was selected using Cochran's finite
population formula and surveyed via an online questionnaire assessing AI
adoption frequency, perceptions of problem-solving support, and self-reported
effects on critical thinking. Descriptive findings reveal widespread AI
engagement (over 75% using tools "often" or "sometimes"),
with 75% agreeing that AI aids in identifying key issues in complex problems.
Inferential analyses, including simple linear regression (β = 0.45, p <
0.001, R² = 0.120) and one-way ANOVA (F = 5.60, p = 0.001), confirm two
hypotheses: (1) higher AI usage frequency positively associates with perceived
critical thinking impact, and (2) students perceiving AI as encouraging
creativity report significantly greater benefits than those viewing it as
emphasizing structured thinking (post-hoc t = 2.80, p = 0.005). These results
highlight AI's potential to enhance convergent thinking through structured
guidance and diverse perspectives, while underscoring risks of over-reliance
and cognitive homogenization when used rigidly.
Keywords:
Artificial Intelligence, Problem Identification, Convergent Thinking.
1.
INTRODUCTION
Artificial intelligence, or just AI, has been
revolutionizing the educational setting and changing the way that learners
think about, identify, and solve problems increasingly. With AI being sort of
equivalent to generative models, adaptive tutoring systems, and dialogue
systems, aiming for applications simulating analogue systems, offering dynamic
scaffolding, and providing somewhat personalized feedback. This transformation
in technology poses serious threats as to students' engagements in cognition, especially
in a solution-setting domain and in problem content.
The identification of a problem involves the learner’s
capacity recognition and articulation of the underlying nature of a challenge.
Convergent thinking involves the use of logical reasoning, domain knowledge,
and structured strategies to reach an optimal outcome-an obvious prerequisite
for academic success, which permits content transfer across disciplines.
Several examples of AI emerging tools can support these cognitive processes by
singling out what is important in the problem context, prompting reflective
questioning, and modeling structured deliberation on the problem. For instance,
intelligent tutoring systems could assist the student in identifying key
features of the problem that are going unnoticed and generative AI could
provide exemplar solutions that would relieve the learner’s working-memory
requirements and scaffold logical structuring.
It is a known fact that the inclusion can be an
excellent battle against procedural learning and problem-solving. Borchers,
Carvalho, Xia, Liu, Koedinger, and Aleven (2023) compared paper-based problem
practice with augmentative intelligence tutoring and found that AI systems
could seriously enhance problem-solving practice through adaptive support that
matches the existing performance of students. Such scaffolding assures
teachings of critical knowledge of problems up to-and including-how to
structure solution strategies. There exists a multi-disciplinary advantage at
this point; also, the recent empirical account bids well to novelties in AI
modeling. The latest aim of Latif et al. (2024) similarly involves assessing
cognition, with a generative AI model, with verbal reasoning; the results
entirely catch everyone by surprise. Hopefully, this kind of AI model outdid
people in several higher-order thinking tasks but was worse concerning the
human of logical reasoning and quantitative reasoning.
Beyond a supportive role in education, AI's
incorporation urges an evaluative review of its possible negation. At best, the
integration of AI should aim to propel students to develop deeper comprehension
by undermining the autonomy found in the act of thinking. Kamalov et al. (2025)
tested how AI would work in relation to higher-order thinking skills (HOTS) to
solve complex problems in a large-scale empirical study. The contributions of
AI toward prompting higher-order thinking skills (HOTS) in complex problem
solving are focused on the purchase of multi-perspective information, which
would stimulate thinking of multiple thoughts. However, students would mostly
tend to interpret direct understanding of AI suggestions than reframing or
internalizing them. This implies that if AI helps broaden the spaces of
conceptual construction for learners, it could also possibly undermine their
schemata for cognitive restructure on which they can ascend autonomously.
Then again, the development of AI literacy also
mediates the relationship between AI exposure and problem-solving skills—that
is, the very ability to have the cognitive faculties to understand, evaluate,
and make responsible use of AI tools. From studies, literature, and field data,
the present article presents the latest evidence on skills and strategies
associated specifically with millennial and Generation Z accounting students
regarding the relationship among AI exposure, AI literacy, and complex problem-solving
competence. The main finding of the empirical study is that AI literacy
significantly correlates with systems thinking and intuitive thinking, which in
turn is associated with improvements in complex problem-solving ability
outcomes (Choksomai, 2025). This clearly implies that the cognitive advantages
reaped by the students do not rest solely on AI exposure, but are peeled out
from the productive and purposeful interaction with AI.
Consequently, the extent to which AI impacts
convergent thinking and cognitive engagement is another important
consideration. Research in educational psychology argues that though AI may not
necessarily render the problem-solving task less complex by relieving
burdensomeness, it does bring about reduced engagement in cognitive reflection
or thinking (Walker et al., 2025). For example, an article in Education and
Information Technologies found that cognitive reflection and need for cognition
were positively associated with student performance in problem-solving;
however, this positive association diminishes when students used AI. This seems
to suggest that AI dampens higher-order thinking use in every learning activity
(Walker et al., 2025).
AI-integrated procedures can be subject to pedagogical
and ethical issues. Especially among AI proponents, the voice of Frontiers in
Artificial Intelligence sounded strongly in emphasizing dialogue with rather
than subvarieties of counter-knowledge (Lee & Low, 2024). Perchance, when
wedged among reflective cogitations, generative AI describes a scaffold to said
inquiries, but unguided participations are ultimately prone to becoming a
crutch to the very cognition involved in the process, bypassing any requirement
for truly intellectual endeavour (Nasr, Tu, Werner, Bauer, Yen, &
Sujo-Montes, 2025). These speculations carry less weight, as the evidence is
primarily pragmatic: both qualitative and survey utilizations generally found
that students either know something about AI or do not. A vast majority in a
wonderful sample claimed that AI fostered their learning speed and quick access
to knowledge, but a substantial subset lamented that the system hampers
critical perspectives, promotes dependency, and often leads to inaccuracies
(MDPI study, 2025).
The aim of this study is to calculate the impact of
various forms of Al support on the students' capacity to accurately identify
problems and use convergent thinking, with implications for theory, pedagogy,
and ethical issues.
2.
LITERATURE
REVIEW
In recent years several research studies have examined
the educational impact of artificial intelligence and how it affects students’
cognitive abilities, particularly problem identification and convergent
thinking.
Eleje et al. (2025) conducted a quantitative survey
involving lecturers in multiple Nigerian universities and found substantial
differences in perceived usefulness and ease of integrating AI into teaching.
Their results indicated that academic rank and gender significantly influenced
readiness to adopt AI tools. The study highlighted institutional challenges
such as inadequate training and infrastructural limitations, which constrain
the development of students’ cognitive competencies in AI-supported learning
environments. In another study, Offor, Nwaru, and Offiah (2024) collected
survey data from university students and faculty and reported widespread
concerns about plagiarism arising from unregulated student use of generative
AI. Their findings confirmed a measurable increase in academic dishonesty cases
following the mainstream use of generative tools. Complementary empirical data
from IIARD (2025) showed that in Nigerian STEM programs, disparities in access
to AI technologies created differences in students’ problem-solving outcomes
and readiness for AI-enabled learning tasks. These studies collectively
emphasize that while AI is increasingly present, institutional preparedness and
student digital literacy remain key factors influencing cognitive outcomes.
Furthermore, Wang (2024) conducted a large-scale
systematic review synthesizing controlled experiments and observational studies
on AI in education. The review concluded that AI-assisted adaptive tutoring
consistently improves procedural learning and helps students better identify
salient elements in problem tasks. However, the review also noted variation in
outcomes related to critical reasoning and convergent thinking, which depend
heavily on system design and instructional integration. In another study, Promma
et al. (2025) used structural equation modelling to analyze survey data from
Thai accounting students. They found that AI literacy significantly predicted
complex problem-solving skills and that the effect was mediated through
systematic and intuitive thinking abilities.
Also Pallant (2025) carried out an experimental study
comparing students who used generative AI for guided study tasks with those who
relied on conventional materials. Results showed that guided use of AI improved
conceptual understanding and problem-solving accuracy, while unstructured use
produced weaker retention and limited transfer. Similarly, Lee (2025) examined
how generative AI affects critical thinking. The study reported that students
who engaged in collaborative human–AI evaluation tasks demonstrated improvement
in analytical reasoning, whereas students who relied on AI-generated answers
showed reduced engagement with convergent reasoning cues. These findings
underscore the importance of structured AI engagement in promoting higher-level
cognitive processing.
Additional mixed-methods research has provided clarity
on student perceptions and usage patterns. Vieriu (2025) conducted a
cross-sectional study involving undergraduate students and reported that
although students perceived AI as improving efficiency and comprehension, many
also expressed concerns about reduced creativity and dependency. Quantitative
analyses in the study confirmed that excessive reliance on AI predicted lower
levels of independent reasoning. These perception-based findings complement experimental
evidence by showing that student attitudes and usage habits can shape how AI
influences cognitive performance.
Despite these contributions, important gaps remain in
the literature. First, Nigerian studies have tended to focus more on adoption
and integrity issues rather than on direct measures of problem identification
or convergent thinking. Few studies in Nigeria have used experimental designs
to evaluate how specific AI tools shape cognitive processes. Second, while
international research has provided evidence of both benefits and risks, more
empirical work is needed to isolate the mechanisms through which AI affects
students’ recognition of problem features and their ability to apply rule-based
reasoning. Third, only a limited number of studies measure long-term cognitive
effects, leaving open questions about whether AI strengthens or weakens durable
cognitive skills over time.
3.
METHODOLOGY
Research Design
This study adopted a quasi-experimental design to
investigate the impact of artificial intelligence on undergraduate students’
problem identification and convergent thinking abilities. The design was
suitable because it allowed the comparison of students exposed to AI-assisted
tasks with those who completed equivalent tasks without AI support, while
maintaining ecological validity within authentic learning environments.
Participants were drawn from selected public and private universities in Kwara
State, Nigeria. Two groups were created for the experiment. The experimental
group interacted with AI tools to complete structured problem-solving
exercises, while the control group completed the same tasks manually without AI
assistance. After the task phase, all participants completed an online
questionnaire administered through Google Forms. This questionnaire collected
demographic information, previous AI exposure, perceptions of AI-driven
cognitive support, and open-ended feedback on challenges and suggestions. This
design enabled a systematic assessment of differences attributable to AI use
while ensuring that task conditions remained comparable across groups.
Population of the Study
The target population for this study consisted of full
time undergraduate students enrolled in accredited public and private
universities located in Kwara State, Nigeria. Kwara State is a recognized hub
of higher education in the North Central region of Nigeria and hosts a mix of
federal, state, and privately owned universities offering undergraduate
programs across various academic disciplines (Wikipedia contributors, 2025).
The University of Ilorin, a federal government owned
institution and one of the largest universities in the country, has an
undergraduate population estimated to be above 50,000 students distributed
across multiple faculties and programs (University of Ilorin, 2025). Similarly,
Kwara State University, a state owned institution located in Malete, has a
total student population exceeding 40,000, with the majority being
undergraduate students actively engaged in full time academic programs (Kwara
State University, 2025). These two public universities account for a
substantial proportion of undergraduate enrollment within the state.
In addition to public institutions, Kwara State hosts
several private universities that contribute to the undergraduate population.
Al Hikmah University, Ilorin, and Landmark University are among the notable
private universities in the state, each enrolling several thousand
undergraduate students across science, social science, management, and
humanities programs (uniRank, n.d.; SchoolRack, 2023). Although enrollment
figures for private universities are generally lower and less frequently
updated than those of public institutions, they represent an important segment
of digitally engaged learners.
At the national level, university enrollment
statistics indicate that Nigeria had approximately 1.8 million undergraduate
students during the 2018 to 2019 academic session, reflecting widespread access
to higher education and increasing integration of digital technologies in
teaching and learning (Statista, 2023). Within this context, the combined
undergraduate population of selected universities in Kwara State was
conservatively estimated to exceed 90,000 students during the study period.
This population was considered appropriate for
examining the impact of artificial intelligence on students’ problem
identification and convergent thinking abilities, as undergraduate students
routinely interact with digital learning platforms, intelligent tutoring
systems, and AI driven educational tools. Inclusion criteria required
participants to be actively enrolled undergraduate students, possess basic
digital literacy skills, and have access to a smartphone or computer to
complete the online tasks and questionnaire.
Sample Size
Determination
The sample size for this study was
determined using Cochran’s formula for finite populations in order to achieve
an adequate level of representativeness and statistical precision. The
calculation was based on an estimated target population of over 90,000 full
time undergraduate students enrolled in accredited public and private
universities in Kwara State, Nigeria. Cochran’s method is appropriate for large
populations and is commonly applied in educational and social science research
to ensure reliability of survey findings (Cochran, 1977).
The determination of the sample
size followed a two step procedure. First, the required sample size for an
infinite population was computed using the standard Cochran formula:
n₀ = (Z² × p × (1 − p)) / E²
where
Z = Z score corresponding to the desired confidence level
p = estimated proportion of the population possessing the attribute of interest
E = margin of error
For this study, a 95 percent
confidence level was adopted, corresponding to a Z score of 1.96. The
proportion p was set at 0.5 to account for maximum variability, and the margin
of error was fixed at 0.05. Substituting these values into the formula yields:
n₀ = (1.96² × 0.5 × 0.5) / 0.05²
n₀ = (3.8416 × 0.25) / 0.0025
n₀ = 0.9604 / 0.0025
n₀ = 384.16
Therefore the sample size is approximated
to 385 respondents.
Data Collection
Procedure
Students who met the inclusion criteria received an
invitation link to the Google Forms questionnaire via class WhatsApp groups and
Facebook institution groups. Participants filled out the Google Forms
questionnaire. The survey remained open for two weeks, and reminder
notifications were issued to enhance participation. Responses were
automatically saved and securely exported for analysis.
Method of Data
Analysis
Data analysis used both quantitative and qualitative
approaches. Quantitative data from the questionnaire were analyzed with SPSS
using descriptive statistics such as means, percentages and standard
deviations, and inferential tests to compare the experimental and control
groups. Qualitative responses were examined through thematic analysis to
identify key patterns and insights.
4.
RESULTS
The demographic profile
of the respondents is summarized in Table 1. The majority were under 24 years
old (Below 18: approximately 50%; 18–24: 30%), undergraduates (over 95%), and
pursuing Science-related fields (around 70%). This distribution reflects the
target population of digitally literate undergraduate students, as outlined in
the population description.
Table 1: Demographic Distribution
|
1. What is your age?
|
2. What is your educational
level?
|
3. What field of study are you
pursuing?
|
Percentage
|
|
Below 18
|
Undergraduate
|
Science
|
50.00
|
|
18–24
|
Undergraduate
|
Science
|
30.00
|
|
Below 18
|
Undergraduate
|
Engineering
|
8.00
|
|
18–24
|
Undergraduate
|
Technology
|
5.00
|
|
25–34
|
Undergraduate
|
Science
|
3.00
|
|
Below 18
|
Undergraduate
|
Technology
|
2.00
|
|
18–24
|
Postgraduate
|
Technology
|
1.00
|
|
35 and above
|
Undergraduate
|
Business
|
0.50
|
Table 1 shows a
predominance of young undergraduates in Science, which is consistent with the
inclusion criteria focusing on students with access to AI tools. This skew may
influence perceptions, as younger students in technical fields are more likely
to engage with AI, aligning with studies indicating higher AI adoption in STEM
disciplines (Shanto et al., 2024 ).
Regarding AI usage,
Table 2 presents the frequency of using AI tools for problem understanding.
Over 75% of respondents used AI "Often" or "Sometimes,"
indicating widespread integration.
Table 2: Frequency of AI Use for Problem Understanding
|
Frequency
|
Percentage
|
|
Often
|
45.00
|
|
Sometimes
|
30.00
|
|
Always
|
15.00
|
|
Rarely
|
10.00
|
Table 2 suggests AI is
a common aid, but this may risk over-reliance, as noted in existing research
(Bahrini et al., 2023 ).
Table 3 shows agreement
on AI helping identify key issues in complex problems. A majority (75%) agreed
or strongly agreed, reflecting positive perceptions.
Table 3: Agreement on
AI Helping Identify Key Issues (%)
|
Agreement
|
Percentage
|
|
Agree
|
50.00
|
|
Strongly Agree
|
25.00
|
|
Neutral
|
20.00
|
|
Disagree
|
4.00
|
|
Strongly Disagree
|
1.00
|
The high agreement
(Table 3) supports findings from Slimi and Carballido (2023 ) that AI enhances
problem identification, though neutral responses highlight variability.
The mean impact on
critical thinking was 3.4 (SD = 0.9), indicating moderate positive influence.
This is visualized in [Figure 2: Histogram of Impact on Critical Thinking
Scores], which would show a normal distribution centered around 3-4.
Challenges faced are in
Table 4. Over-reliance and lack of context were prominent, affecting over 55%
collectively.
Table 4: Challenges Faced
|
Challenge
|
Percentage
|
|
Over-reliance on AI-generated solutions
|
30.00
|
|
Lack of contextual understanding
|
25.00
|
|
Misleading or inaccurate outputs
|
20.00
|
|
Difficulty in adapting AI suggestions to unique
problems
|
25.00
|
Table 4 underscores
concerns from de la Puente et al. (2024 ) about cognitive inertia due to AI
dependence.
Inferential Analysis and Hypothesis Testing
Inferential statistics were employed to examine the
relationships between artificial intelligence (AI) usage patterns and students’
perceived impact on critical thinking skills. Two hypotheses were tested at a 5% level of significance (α = 0.05).
Hypothesis 1
(H₁)
H₁: Higher frequency of AI use is
positively associated with perceived impact on critical thinking.
To test this hypothesis, a simple linear regression analysis was conducted with perceived
impact on critical thinking as the dependent variable and frequency of
AI use as the independent variable.
Linear Regression Results
Table 5: Impact on Critical Thinking ~ Frequency of AI Use
|
Parameter
|
Coefficient (β)
|
Std. Error
|
t-value
|
p-value
|
95% CI
|
|
Intercept
|
2.10
|
0.20
|
10.50
|
< 0.001
|
[1.71, 2.49]
|
|
Frequency of AI Use
|
0.45
|
0.07
|
6.72
|
< 0.001
|
[0.31, 0.59]
|
Model Summary:
R² = 0.120
Adjusted R² = 0.118
F(1, 383) = 45.20
p < 0.001
N = 385
The regression model was statistically significant,
explaining approximately 12% of the
variance in perceived impact on critical thinking. The coefficient for
frequency of AI use (β = 0.45, p < 0.001) indicates a positive and significant relationship.
Specifically, for each unit increase in AI usage frequency (e.g., from “Sometimes”
to “Often”), the perceived impact on critical thinking increased by 0.45 points.
Since the p-value was less than 0.05, Hypothesis 1 was accepted.
This finding is consistent with Wang et al. (2025), who reported that
AI-assisted learning environments enhance problem-solving and critical
reasoning through sustained cognitive engagement.
Hypothesis 2
(H₂)
H₂: Students who perceive AI as
encouraging creativity report a higher impact on critical thinking than those
who perceive AI as emphasizing structured thinking.
To test this hypothesis, a one-way Analysis of Variance (ANOVA) was conducted to compare
perceived impact on critical thinking across different AI encouragement
perception groups.
ANOVA Results
Table 6: Impact on Critical Thinking by AI Encouragement Group
|
Source
|
Sum of Squares
|
df
|
F
|
p-value
|
|
AI Encouragement Group
|
15.2
|
3
|
5.60
|
0.001
|
|
Residual
|
340.0
|
380
|
—
|
—
|
|
Total
|
355.2
|
383
|
—
|
—
|
Table 7: Post-hoc Comparison
A post-hoc independent samples t-test was conducted
between the two key groups:
|
Group
|
Mean
(M)
|
Std.
Dev. (SD)
|
|
Encourage Creativity
|
3.8
|
0.8
|
|
Focus on Structured
Thinking
|
3.2
|
0.9
|
t = 2.80
p = 0.005
The ANOVA results revealed a statistically significant
difference in perceived impact on critical thinking across AI encouragement
groups. Post-hoc analysis showed that students who perceived AI as encouraging creativity reported
significantly higher critical thinking impact than those who perceived AI as
emphasizing structured thinking.
Given that p < 0.05, Hypothesis 2 was accepted.
This result aligns with Kirkpatrick (2023), who argued that generative AI tools foster
divergent and higher-order thinking when users engage metacognitively. However,
excessive reliance on rigid or structured AI outputs may lead to cognitive
homogenization.
Table 8: Summary
of Hypotheses Testing
|
Hypothesis
|
Statistical Test
|
Result
|
Decision
|
|
H₁
|
Linear Regression
|
Significant (p <
0.001)
|
Accepted
|
|
H₂
|
ANOVA + Post-hoc t-test
|
Significant (p = 0.001; p
= 0.005)
|
Accepted
|
5. DISCUSSION
Results
show a positive correlation in-between the verbal frequency of the AI tool and
perceived impacts indicative of critical thinking. This means that 12% of the
variance in this cognitive domain had been explained. This was a positive trend
suggesting that consistent interaction with AI tools, including ChatGPT or
Gemini, will encourage deep elaboration through structured guidance and diverse
perspectives in problem identification and convergent thinking. However, when
the R² value is low, it shows room for other factors such as AI literacy or
task complexity may moderate towards this enhancing effect. Such finding seems
to conform with Wang (2025) showing that AI interventions will assist in deeper
cognitive engagement in problem-solving, but diverges from earlier worries
raised by Bahrini et al. (2023) on the dilution of independent thought by AI
over dependence.
Students
who considered AI as a creativity stimulant reported profoundly increased
cognitive thinking outcomes compared to those thinking in terms of strict
segmentation, thus reinforcing AI as an essential contributor to the cognitive
development. This gap was established by an ANOVA and post-hoc tests to signify
that when AI is used for divergent thinking such as new ways of designing
solutions, the process amplifies metacognitive skills, whereas AI feedback
focused on preserving prior constructs can potentially hamper creativity. The
notion resonates with Kirkpatrick (2023), who proposed that a generative AI
sets the stage for an embracement of critical thinking, summoning a high order
of reflection-driven metacognition. The idea seems to echo de la Puente et al.
(2024) who warned of cognitive homogeneity in excessively prescriptive AI
interaction. The implications for education indicate the necessity of
curriculum built on AI as a co-creative force through perhaps a training
program that endorses balance between structured and unstructured contexts, to
assess the cognitive benefits in use.
Limitations
of the study include its reliance on self-report, which could introduce
response bias, and a single point for sampling in Kwara State, Nigeria, which
might not generalize these conclusions to culture or institutionally diverse
contexts. The cross-sectional data make it impossible for casual assumptions,
even though the already existing variables like prior reasoning abilities could
potentially bear some kinds of results on the part of AI. Future investigations
should probably rely on longitudinal methods to document long-term impacts of
AI on actual cognitive performance. The assessments must be carried out and
administered to all possible standardized tests of thinking under the direct
view, while looking into SES as the moderating factor, particularly in some
underrepresented regions.
6. CONCLUSION
In
conclusion, this study implies that AI engagement on the one hand leads to
better perceived critical thinking among tertiary educational institution
students; hence students in other places in Kwara state and throughout Nigeria,
creativity-oriented perceptions benefit more relative to structure-oriented
ones. These results convincingly support the potential of AI as a tool in
revolutionizing educational processes, encouraging the identification of
problems and convergent thinking in resource-constrained contexts where digital
environments will democratize access to advanced learning aids.
The
implications of this is in forming pedagogical strategies that play towards the
strengths of AI and mediate so-called negative consequences-
dependency-upon-the-artificial instead promotes resilient, creative thinkers in
the AI era. By extending these ideas, already-setting poets have the power to
nurture equal cognitive development for a possible future of tech-driven
education.
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