Smooth and Safe Garage Door Repairs in Calgary NE

 Garage doors play a significant and often underestimated role in the functionality and appearance of a home. Beyond being a point of entry, a well-maintained garage door enhances security, protects vehicles and stored belongings from weather damage, contributes to curb appeal, and supports energy efficiency. When something goes wrong—whether it’s a loud grinding sound, an unresponsive opener, uneven door movement, or a broken spring—it disrupts daily routines and compromises safety. That’s why homeowners in Calgary NE place high value on dependable garage door repair services that respond quickly and professionally when issues arise. 

When a garage door begins to malfunction, one of the most common causes is wear and tear. Springs, rollers, hinges, and cables all undergo constant tension and friction every time the door opens and closes. Over time, these components weaken and can suddenly fail. A snapped cable or broken torsion spring is not just an inconvenience—it can be dangerous. Garage doors are extremely heavy, and without proper counterbalance, they can slam shut unexpectedly, posing risks to people, pets, and property. Professional technicians trained in garage door systems ensure that repairs are completed safely and according to manufacturer specifications.

Weather also plays a major role in garage door damage. Calgary’s climate is known for temperature swings, freezing winters, chinook winds, and moisture fluctuations. These conditions impact metal components, weather seals, and tracks. Rust can form, rubber seals can harden or crack, and lubrication may dry out more quickly than in milder regions. Homeowners in Calgary NE often notice that their garage doors become noisy or slow during the winter months because the cold affects the opener motor and metal hardware. Choosing reliable service providers specializing in garage door repair Calgary NE ensures that technicians understand the local climate’s demands and can recommend long-lasting solutions.

Opener-related issues are also common. Modern openers include safety sensors, remote systems, and digital access controls that sometimes malfunction due to programming errors, sensor misalignment, or electrical problems. Attempting to repair openers without knowledge of electrical systems can create more damage or void warranties. Skilled professionals can diagnose whether an opener requires repair, recalibration, or full replacement depending on its age and condition.

Homeowners focused on property value also understand that a functioning garage door contributes to curb appeal. A misaligned or dented door can make a home look outdated or neglected. Repairs such as panel replacement, repainting, weather seal upgrades, or track alignment not only restore operation but improve aesthetics. In competitive real estate markets, these upgrades can help a home stand out and sell more quickly.

Many customers are also interested in preventative maintenance rather than waiting for major breakdowns. Preventative maintenance typically includes inspection of tracks, tightening of hardware, lubrication of rollers and hinges, testing of opener safety systems, and replacement of worn weather stripping. These small adjustments help reduce repair frequency and extend the lifespan of the garage door system.

When selecting a repair company, homeowners look for transparency, experience, fast response times, and high-quality replacement parts. A reputable service provider should offer clear assessments of the issue, explain repair options, and provide fair pricing without hidden fees. Having access to local technicians also makes emergency services more efficient, especially when a garage door becomes stuck halfway or refuses to close in the evening.

For homeowners seeking reliable and efficient services, local specialists in garage door repair Calgary NE provide expertise tailored to the needs of the area. From spring replacements and cable repairs to opener diagnostics and full door servicing, they handle the full range of mechanical and electrical issues that garage doors experience over time.

In the end, timely repair and maintenance not only restore convenience—they ensure safety, protect property, and maintain the value of the home. A malfunctioning garage door should never be ignored, especially when professional help is readily available. By relying on experienced technicians who understand Calgary NE homes and weather conditions, property owners can keep their garage doors operating smoothly, quietly, and securely throughout the year.

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Map Wall Art for Global Teams | City, Region & Route Prints

Global teams work across time zones, projects, and screens. A well-chosen map can help everyone feel connected to the same mission, even when the team is spread out. Map wall art is also practical: it can show where key teams work, where customers are served, and how ideas move from planning to launch. When done well, it reads like a clear visual summary rather than a busy poster. 

This guide explains how to choose city maps, region maps, and route maps for team spaces, plus how to plan size, layout, and placement. If you want a starting point for options and styles, explore our maps wall art collection and note the formats you want to repeat across your office walls.

Why Map Wall Art Works for Distributed Teams

A shared reference point in the room

In a conference room or a home office, a map can act as a “shared screen” that does not need power. It helps new teammates learn context quickly: markets, partner areas, and the paths your work supports. For leadership meetings, a map can keep planning grounded in real service areas instead of vague statements.

A culture piece that also supports work

Many teams want wall decor that looks professional while still saying something about who they are. Map canvas art can do both: it is artwork, and it can hold meaning for hiring, onboarding, and team events. Instead of hanging random paintings, a map wall print can reflect your company story and make the space feel intentional.

A quiet tool for onboarding and alignment

When someone joins a distributed team, they often hear a lot of names: teams, time zones, markets, and partners. A map placed near the meeting area can reduce confusion. It gives a new teammate a fast way to understand how the organization thinks about coverage and growth, and it can support introductions during weekly check-ins.

Cities vs. Regions vs. World Maps: Choose the Right Format

The best format depends on what you want the room to communicate. Use this quick list to pick your direction before you think about colors and framing.

• City map art: Best for a main hub, a flagship office, or a team “home base” you talk about often.
• Region map print: Best for territories, service zones, and planning across a wider area without tiny labels.
• World map wall art: Best when your story is scale: remote teams, global partners, or a broad customer base.

If you want a city-focused look without listing geographic names in your copy, you can still style the piece around “team hubs” and “key markets” while keeping the design clean. For more city-map formats, browse our cities wall art collection and use it as a reference for layout ideas.

Custom Routes That Turn Team History Into Wall Art

Route-based map art is a strong choice for teams that track milestones: first launch, major partnership, new office, or a yearly offsite. Routes can be literal travel paths, or they can be symbolic lines that connect project stages. The key is to keep routes readable from across the room.

Route concepts that work well in offices

Try route designs that match how your team already talks about progress. For example, a route can connect phases like “concept,” “build,” and “release,” or it can connect touchpoints like “support,” “sales,” and “product.” A route map can also work as a gift idea for a manager, a founder, or a teammate who is relocating.

What to provide for a route map request

To avoid back-and-forth, prepare a short brief with the details below. This also helps keep the final wall print neat and balanced.

1. Map scope: city-level, region-level, or world-level.
2. Route points: start, end, and any milestone stops you want shown.
3. Label rules: minimal labels, or labels only for stops.
4. Style notes: clean lines, bold blocks, or a more textured print look.
5. Size target: standard canvas print size, large wall art, or a multi-piece set.

Route readability checks

Before finalizing a route design, review it from a distance. If the route disappears, increase line weight. If the route dominates the map, reduce weight or remove minor stops. If labels feel crowded, limit labels to the start and end and place the rest in a small legend.

Design Choices That Keep Map Canvas Prints Clear

Make hierarchy obvious

A map can hold a lot of information, so hierarchy matters. Use one main subject (city grid, region outline, or the route), then support it with only a few secondary details. When everything has the same weight, the map becomes hard to read.

Match the room and the brand

For office wall art, neutral palettes often look consistent across meeting rooms, hallways, and entryways. If you want a stronger brand tie-in, use a limited accent color for route lines or markers. For home spaces, you can choose warmer tones that fit bedroom canvas decor or a living room wall decor plan. If you are building a gallery wall, keep fonts consistent across pieces so the set looks unified.

Plan size by viewing distance

Small prints can work near a desk, but maps are often more effective as large art prints because viewers can read them from farther away. In a conference room, aim for a large wall art size that can be understood from the farthest chair. In a narrow hallway, a horizontal wall hanging can guide the eye along the space without feeling crowded.

Single statement piece vs. a series

If your team has one clear story, one large canvas print may be enough. If you have multiple hubs and multiple story lines, a series can work better: one map for the main hub, one for coverage, and one for routes tied to milestones. A series also makes it easier to update later by replacing just one piece.

Privacy and Clarity for Team Maps

Keep sensitive points broad

Teams sometimes want to mark offices, partners, or events on a map. If privacy is a concern, use general areas rather than pinpoint markers. A map can show a hub or a zone without revealing exact addresses. This approach is also useful for public-facing rooms where visitors may see the artwork.

Use labels only where they help

Labels should support the message, not compete with it. If your map is meant to show coverage, labels can be minimal. If your map is meant to teach onboarding context, labels can be slightly more detailed. The goal is a map that reads quickly in a meeting, not one that requires close study.

Where to Hang Map Wall Decor in Team Spaces

Placement can change how the piece is read. Map artwork works best where people naturally pause, talk, or plan. Use this list to match the map type to the wall.

• Reception or lobby: A world or region map that signals reach at first glance.
• Conference room: A region map or route map that supports planning and review meetings.
• Hallway: A series of map prints that tell a timeline across multiple canvases.
• Entryway: A clean city map that sets the tone as people arrive.
• Lounge or break room: A route map tied to team trips, launches, or milestones.

If you are styling a workplace, it helps to mix map wall art with other office-ready artwork that fits the same visual language. For more pieces built for workspaces, visit our office wall art collection and coordinate map prints with supporting wall decor.

How to Brief a Map Set for a Global Team

Start with one “hero” piece

Choose one main art canvas that carries the core story: the team’s map scope and route theme. This hero piece usually goes in the conference room or the main shared space.

Build supporting pieces around it

Then add smaller wall prints for nearby areas: a city-level piece for a team hub, a region-level piece for a key service zone, or a map-inspired canvas art piece that matches the palette. This approach keeps the room decor consistent without repeating the same map again and again.

Set a naming system for internal use

If you plan to create several pieces over time, set a simple naming system such as “Hub Map,” “Coverage Map,” and “Routes Map.” It makes planning easier, helps teams request updates, and keeps future additions consistent with the first set.

Common Mistakes to Avoid

Over-labeling

Too many labels can make even a large canvas feel busy. Pick only the labels that support your message.

Using too many styles in one room

Mixing multiple map styles can look random. Keep the typography, line weight, and margins consistent so the set feels planned.

Forgetting lighting and glare

Place map art where overhead lights do not reflect strongly on the surface. If you have strong downlights, test the wall angle before final placement.

Wrap-Up: Turn Team Geography Into Meaningful Wall Art

Map art can do more than fill an empty wall. For global teams, it can show scope, connect people to shared projects, and create a consistent look across office and home workspaces. Start by choosing the right map type (city, region, world, or route), plan a brief that keeps details clear, then place the canvas where teams naturally meet and talk.

When you are ready to choose your next canvas prints, explore map formats, sizes, and matching wall art prints so your space tells one clear story.

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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.

References

Afolabi, A. (2024). Ethical issues in artificial intelligence adoption in African higher education institutions in Nigeria. African Journal of Information and Knowledge Management, 3(2), 22–33. https://doi.org/10.47604/ajikm.2735

Akeke, M. N. G., Atah, C. A., & Bessong, E. (2025). Adoption and utilization of artificial intelligence in teaching business education content in the twenty-first century. Nigerian Journal of Business Education, 12(4), 125–138.

Alowooja, T. O., & Ajibola, M. A. (2025). Enhancing pedagogical delivery through AI-powered personalized learning systems in Nigerian Colleges of Education. Advance Journal of Education and Social Sciences, 10(5), 237–245.

Arabo, I. F., Ahmed, Y., & Abdulmalik, A. (2025). The impact of artificial intelligence on STEM education and workforce readiness in Nigeria. International Journal of Computer Science and Mathematical Theory, 11(6), 74–86. https://doi.org/10.56201/ijcsmt.vol.11.no6.2025.pg74.86

Asiyanbola, C., Ogunlade, O. O., Raji, F. A., & Adebara, S. M. (2025). Educators’ awareness and adoption of artificial intelligence for edupreneurship in University of Ilorin, Kwara State. Ilorin Journal of Education, 45(2), 539–550.

Borchers, C., Carvalho, P. F., Xia, M., Liu, P., Koedinger, K. R., & Aleven, V. (2023). What makes problem-solving practice effective? Comparing paper and AI tutoring. In O. Viberg, I. Jivet, P. Muñoz-Merino, M. Perifanou, & T. Papathoma (Eds.), Responsive and Sustainable Educational Futures (Lecture Notes in Computer Science, Vol. 14200, pp. …). Springer.

Choksomai, S. (2025). The influence of AI literacy on complex problem-solving skills through systematic thinking skills and intuitive thinking skills: An empirical study in Thai Gen Z accounting students. Computers & Education: Artificial Intelligence, 8, Article 100382. https://doi.org/10.1016/j.caeai.2025.100382

Kamalov, F., Song, L., & others. (2025). Facilitator or hindrance? The impact of AI on university students’ higher-order thinking skills in complex problem solving. International Journal of Educational Technology in Higher Education, 22, Article 39. https://doi.org/10.1186/s41239-025-00534-0

Kirkpatrick, K. (2023). Can AI demonstrate creativity? Communications of the ACM, 66(2), 21–23. https://doi.org/10.1145/3575665

Latif, E., Zhou, Y., Guo, S., Gao, Y., Shi, L., Nayaaba, M., Lee, G., Zhang, L., Bewersdorff, A., Fang, L., Yang, X., Zhao, H., Jiang, H., Lu, H., Li, J., Yu, J., You, W., Liu, Z., Wang, H., … Zhai, X. (2024). A systematic assessment of OpenAI o1-Preview for higher-order thinking in education. arXiv. http://dx.doi.org/10.48550/arXiv.2410.21287

Lee, C.-C., & Low, M. Y. H. (2024). Using genAI in education: The case for critical thinking. Frontiers in Artificial Intelligence, 7, Article 1452131. https://doi.org/10.3389/frai.2024.1452131

Nasr, N. R., Tu, C.-H., Werner, J., Bauer, T., Yen, C.-J., & Sujo-Montes, L. (2025). Exploring the impact of generative AI ChatGPT on critical thinking in higher education: Passive AI-directed use or human–AI supported collaboration? Education Sciences, 15(9), 1198. https://doi.org/10.3390/educsci15091198

Offor, U., Nwaru, E., & Offiah, J. (2024). Excessive use and academic integrity concerns: Empirical findings from Nigerian universities. IJIISTR Conference Report.

Vieriu, A. M. (2025). The impact of AI on students’ learning and perceptions: A mixed–methods study. Education Sciences, 15(3), 343–359.

Wang, S. (2024). Artificial intelligence in education: A systematic literature review. Expert Systems with Applications, 214, Article 119146.

Wang, J., & Fan, W. (2025). The effect of ChatGPT on students' learning performance, learning perception, and higher-order thinking: Insights from a meta-analysis. Humanities and Social Sciences Communications, 12(1), Article 621. https://doi.org/10.1057/s41599-025-04787-y

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