INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN SOCIAL SCIENCE (IJRISS)  
ISSN No. 2454-6186 | DOI: 10.47772/IJRISS | Volume IX Issue XXVI November 2025| Special Issue on  
Impact of Artificial Intelligence on Assessment, Engagement and  
Motivation among Secondary School Students in Kaduna State,  
Nigeria  
Dogara, Rahmatu Abdullahi., Fatima Shehu Kabir., Uthman Shehu Lawal  
Department of Education Foundations, Kaduna State University, Nigeria  
DOI: https://dx.doi.org/10.47772/IJRISS.2025.903SEDU0710  
Received: 07 November 2025; Accepted: 14 November 2025; Published: 02 December 2025  
ABSTRACT  
This paper examined the impact of Artificial Intelligence (AI) on student motivation and engagement in  
educational assessments. With the growing integration of AI technologies such as adaptive testing, automated  
grading, and intelligent feedback systems, assessment practices are being reshaped to promote efficiency,  
fairness, and personalization. The study reviewed the concept of AI in education, highlighting its potential to  
foster intrinsic motivation, enhance student engagement, and reduce assessment anxiety through real-time  
feedback and adaptive questioning. However, challenges such as ethical concerns, overreliance on technology,  
and possible bias in AI algorithms were also discussed. Findings suggest that while AI-powered assessments  
improve inclusivity, promote continuous learning, and sustain motivation, they also risk diminishing critical  
thinking and creativity if not carefully managed. The paper concluded that AI-driven assessments must  
complement, not replace, human judgment, and recommended that educators adopt blended assessment models  
that balance technology with human-centered learning principles.  
Keywords: Artificial Intelligence, Motivation, Engagement, Educational Assessment, Students  
INTRODUCTION  
Education in the 21st century is undergoing a radical transformation driven largely by the infusion of  
technology into teaching, learning, and assessment processes. Among these technologies, Artificial Intelligence  
(AI) has emerged as one of the most influential forces reshaping educational practices. AI refers to computer  
systems that can perform tasks that typically require human intelligence, such as learning, reasoning, decision-  
making, and adapting to new inputs (Russell & Norvig, 2020). Its application in education spans across  
personalized learning systems, intelligent tutoring, automated grading, and adaptive assessments, all of which  
are redefining how students experience learning and evaluation. One of the most significant areas where AI is  
making an impact is in educational assessments. Traditionally, assessments have been used primarily to  
measure student performance, often through standardized tests and summative evaluations.  
However, these conventional approaches are increasingly criticized for encouraging rote memorization,  
fostering test anxiety, and failing to account for individual differences in learning (Selwyn, 2019). Moreover,  
high-stakes testing has been shown to reduce intrinsic motivation, as students often focus more on grades than  
on the actual learning process (Ryan & Deci, 2020). AI-driven assessments, by contrast, promise to provide  
more dynamic, personalized, and student-centered evaluation systems that not only measure learning but also  
stimulate motivation and engagement. AI-powered assessments employ adaptive algorithms that tailor  
questions to the student’s ability level, ensuring that tasks are neither too easy nor too difficult (Luckin,  
Holmes, Griffiths, & Forcier, 2016). This adaptive mechanism helps sustain student interest and minimizes  
frustration, thus enhancing engagement. Additionally, AI provides instant and detailed feedback, allowing  
learners to recognize their strengths and weaknesses immediately. Research indicates that timely feedback is a  
crucial determinant of motivation and persistence in learning (Hattie & Timperley, 2007). By offering feedback  
that is specific, actionable, and personalized, AI promote a sense of competence and self-efficacy, which are  
essential components of intrinsic motivation (Deci & Ryan, 2000).  
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Beyond feedback, AI also facilitates continuous and formative assessment, which contrasts with the episodic  
and high-pressure nature of traditional summative tests. Continuous assessment encourages students to view  
learning as an ongoing process rather than a one-time evaluation event (Holmes, Bialik, & Fadel, 2021). This  
shift can reduce performance anxiety and encourage deeper learning engagement. For example, intelligent  
tutoring systems not only assess knowledge but also adapt instructional strategies to keep learners motivated  
and on track. These systems transform assessments from being punitive measures into supportive tools that  
guide learners toward mastery.  
Despite these advantages, there are concerns about the increasing reliance on AI in assessment. Critics warn  
that algorithmic bias, lack of transparency in scoring, and the potential reduction of human interaction may  
negatively affect student motivation in the long term (Howard & Borenstein, 2018). Furthermore,  
overdependence on automated systems may undermine creativity and critical thinking if assessments become  
too mechanistic. Ethical concerns such as data privacy, fairness, and the psychological impact of constant  
monitoring also pose challenges that educators must address (Kaplan & Haenlein, 2019). The integration of AI  
into assessments is still at an emerging stage, but the potential benefits are significant. With large class sizes  
and limited teaching resources, AI-driven assessments could provide efficient and scalable solutions for  
monitoring student progress and providing personalized feedback. However, to realize these benefits,  
educators must ensure that AI complements rather than replaces human judgment. Teachers and counsellors  
remain vital in nurturing student motivation, providing socio-emotional support, and interpreting assessment  
results within a holistic framework (UNESCO, 2021).  
Statement of the Problem  
Assessment is a fundamental component of education, designed to evaluate student performance, provide  
feedback, and guide instructional planning. However, traditional assessment systems are often rigid, stressful,  
and fail to capture individual learning differences, leading to demotivation and disengagement among students.  
With the growing adoption of AI-driven assessment tools, there is potential to transform this landscape by  
providing adaptive, personalized, and engaging learning experiences.  
Despite this promise, uncertainties remain about how AI impacts motivation and engagement. Does AI  
genuinely improve students’ intrinsic drive to learn, or does it encourage dependency on automated systems?  
Can AI-based assessments reduce test anxiety, or do they create new pressures associated with technological  
reliance? These questions highlight the need for systematic inquiry. In the Nigerian context, limited research  
has been conducted on the relationship between AI and student engagement in assessments. This study  
therefore seeks to fill this gap by analyzing the motivational and engagement outcomes of AI-driven  
assessments.  
Objectives of the Study  
1. To examine the influence of AI-driven assessments on students’ motivation to learn.  
2. To analyze the effects of AI-based feedback on students’ engagement in learning activities.  
3. To identify challenges and ethical considerations in the use of AI for educational assessments.  
4. To suggest strategies for integrating AI into assessment practices without undermining student  
creativity and intrinsic motivation.  
Research Questions  
1. To what extent do AI-driven assessments influence students’ motivation?  
2. How does AI-based feedback affect student engagement in educational activities?  
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Hypotheses  
Ho1: AI-driven assessments have no significant effect on students’ motivation.  
Ho2:There is no significant relationship between AI-based feedback and student engagement.  
Ho3: There are no effective strategies for integrating AI in assessments without undermining motivation and  
creativity.  
METHODOLOGY  
This study employed a descriptive survey research design. This design was selected because it provides an  
accurate description of existing conditions, practices, and attitudes among a defined population (Creswell &  
Creswell, 2018). The survey approach was appropriate since the study sought to measure students’ perceptions  
of the effect of Artificial Intelligence (AI) on their motivation and engagement in assessments. The target  
population comprised all SSII students of the Demonstration Secondary School, Ahmadu Bello University  
(ABU), Zaria, during the 2024/2025 academic session which stands at a total of 480 students. The sample size  
was determined using Yamane’s (1967) formula for finite populations at a 5% margin of error, giving a sample  
of 214 students which was selected using simple random sampling technique. The study used a structured  
questionnaire titled Artificial Intelligence and Student Motivation/Engagement Scale (AISMES). The  
instrument consisted of 25 items on a 5-point Likert scale ranging from Strongly Agree (5) to Strongly Disagree  
(1). Three experts in Educational Psychology and Measurement from Ahmadu Bello University, Zaria  
validated the instrument. A pilot test with 30 students outside the sample produced a Cronbach’s Alpha  
coefficient of 0.84, indicating good internal consistency (George & Mallery, 2019). The researcher personally  
administered the questionnaire with assistance from class teachers. Out of 214 copies distributed, 200 were  
retrieved, representing a 93% response rate. Data was analyzed using descriptive statistics (frequency,  
percentage, mean, and standard deviation) to answer research questions, while Chi-square test and Independent  
t-test were used to test the hypotheses at the 0.05 level of significance.  
RESULTS  
Research Question One: To what extent do AI-driven assessments influence students’ motivation?  
Table 1: Students’ Perception of AI-driven Assessments on Motivation  
Response  
Frequency  
Percentage  
41%  
Strongly Agree (SA)  
Agree (A)  
82  
74  
20  
16  
8
37%  
Neutral (N)  
10%  
Disagree (D)  
Strongly Disagree (SD)  
Total  
8%  
4%  
200  
100%  
Mean = 3.95, SD = 0.84  
Majority of students (78%) agreed that AI-driven assessments improved their motivation.  
Research Question Two: How does AI-based feedback affect student engagement in educational activities?  
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Table 2: Effects of AI-based Feedback on Student Engagement  
Response  
Frequency  
Percentage  
38%  
Strongly Agree (SA)  
Agree (A)  
76  
80  
18  
16  
10  
200  
40%  
Neutral (N)  
9%  
Disagree (D)  
Strongly Disagree (SD)  
Total  
8%  
5%  
100%  
Mean = 3.98, SD = 0.79  
About 78% of students reported increased engagement with AI-driven feedback.  
Hypotheses Testing  
Hypothesis One: Ho₁: AI-driven assessments have no significant effect on students’ motivation.  
To test this hypothesis, responses to items measuring motivation were analyzed using the Chi-square (χ²) test  
of independence.  
Variable  
N
df  
χ²-cal  
p-value Decision  
0.000 Significant (Reject Ho₁)  
AI-driven Assessments × Motivation 200  
4
32.84  
Table 3 shows that the calculated Chi-square (χ²) value of 32.84 was greater than the Chi-square critical value  
of 9.49 at 4 degrees of freedom and 0.05 level of significance. Since the calculated value exceeded the critical  
value, the null hypothesis which states that AI-driven assessments have no significant effect on students’  
motivation was rejected, while the alternative hypothesis which states that AI-driven assessments have a  
significant effect on students’ motivation was accepted. This implies that AI-based assessments significantly  
enhance students’ motivation in learning, as they make evaluation more interactive and reduce test anxiety. The  
finding agrees with Hattie and Timperley (2007) who emphasized that timely and specific feedback increases  
students’ confidence and persistence, and Luckin et al. (2016) who noted that adaptive AI systems sustain  
motivation by adjusting question difficulty to individual ability levels.  
Hypothesis Two (Ho₂): There is no significant relationship between AI-based feedback and student  
engagement.  
This hypothesis was tested using an Independent Samples t-test, comparing the mean engagement scores of  
students who reported high exposure to AI feedback with those who reported low exposure.  
Group  
N
Mean  
4.12  
SD  
t-cal  
df  
p-value Decision  
Significant (Reject Ho₂)  
High AI-feedback Group 100  
Low AI-feedback Group 100  
0.65  
0.81  
4.62  
198 0.000  
3.72  
Table 4 shows that the calculated t-value of 4.62 was greater than the t-critical value of 1.96 given 198 degrees  
of freedom at 0.05 level of significance. Since the calculated value was greater than the critical value, the null  
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hypothesis which states that there is no significant relationship between AI-based feedback and student  
engagement was rejected, while the alternative hypothesis which states that there is a significant relationship  
between AI-based feedback and student engagement was accepted. This implies that AI feedback significantly  
enhances students’ participation and engagement in learning tasks. This finding supports Fredricks,  
Blumenfeld, & Paris (2004) who identified feedback as a major driver of cognitive and behavioral  
engagement, and Lee & Hammer (2011) who asserted that gamified AI assessments sustain student enthusiasm  
and persistence in completing tasks.  
Hypothesis Three: There are no effective strategies for integrating AI in assessments without undermining  
motivation and creativity.  
To test this hypothesis, responses related to proposed strategies (e.g., teacher training, balanced integration,  
ethical safeguards) were analyzed using descriptive mean analysis and a Chi-square test to determine whether  
respondents agreed on the effectiveness of the strategies.  
Variable  
N
df χ²-cal  
29.45  
p-value Decision  
0.001 Significant (Reject Ho₃)  
AI Integration Strategies × Motivation/ Creativity 200 4  
Table 3 reveals that the calculated Chi-square (χ²) value of 29.45 was greater than the Chi-square critical value  
of 9.49 at 4 degrees of freedom and 0.05 level of significance. Since the calculated value exceeded the critical  
value, the null hypothesis which states that there are no effective strategies for integrating AI in assessments  
without undermining motivation and creativity was rejected, while the alternative hypothesis which states that  
there are effective strategies for integrating AI without undermining motivation and creativity was accepted.  
This implies that effective strategies such as teacher professional training, balanced AI-human assessment  
models, and ethical safeguards exist to integrate AI responsibly. This is in line with UNESCO (2021) which  
emphasized the need for human-centered AI integration in education, and Selwyn (2019) who cautioned that  
overreliance on automation should be mitigated through ethical oversight and teacher involvement.  
Summary of Hypotheses Testing  
Hypothesis  
Statistical  
Used  
Test Result  
Decision  
Ho₁  
Ho₂  
Ho₃  
Chi-square  
t-test  
χ² = 32.84, p < 0.05 Rejected  
t = 4.62, p < 0.05 Rejected  
χ² = 29.45, p < 0.05 Rejected  
Chi-square  
Research Question One  
To what extent do AI-driven assessments influence students' motivation?  
This study investigated AI-driven assessments using adaptive learning platforms (specifically Moodle Quiz  
with Machine Learning capabilities, Quizizz AI, and Kahoot! Smart Practice) that provide real-time feedback  
and adjust question difficulty based on student performance.  
Table 1: Students' Perception of AI-driven Assessments on Motivation  
Response  
Frequency  
Percentage  
Strongly Agree (SA)  
82  
41%  
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Agree (A)  
74  
20  
16  
8
37%  
10%  
8%  
Neutral (N)  
Disagree (D)  
Strongly Disagree (SD)  
Total  
4%  
200  
100%  
Mean = 3.95, SD = 0.84  
The majority of students (78%) agreed that AI-driven assessments improved their motivation. The adaptive  
nature of AI assessments supports perceived competence by providing appropriately challenging tasks, while  
immediate feedback enhances autonomy through self-paced learning.  
Research Question Two  
How does AI-based feedback affect student engagement in educational activities?  
AI-based feedback in this study refers to automated, personalized responses generated by intelligent tutoring  
systems (ITS) and formative assessment tools that analyze student responses and provide tailored suggestions  
for improvement.  
Table 2: Effects of AI-based Feedback on Student Engagement  
Response  
Frequency  
Percentage  
38%  
Strongly Agree (SA)  
Agree (A)  
76  
80  
18  
16  
10  
200  
40%  
Neutral (N)  
9%  
Disagree (D)  
Strongly Disagree (SD)  
Total  
8%  
5%  
100%  
Mean = 3.98, SD = 0.79  
About 78% of students reported increased engagement with AI-driven feedback. AI feedback systems appear  
to enhance all three dimensions by promoting active participation (behavioral), sustaining interest (emotional),  
and encouraging deeper processing of content (cognitive).  
Hypotheses Testing  
Hypothesis One: : AI-driven assessments have no significant effect on students' motivation.  
Responses to items measuring motivation were analyzed using the Chi-square ( ) test of independence,  
grounded in Self-Determination Theory to examine how AI assessments influence intrinsic and extrinsic  
motivation.  
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Table 3: Chi-square Test Results for AI-driven Assessments and Motivation  
Variable  
N
df  
p-value  
Decision  
-cal  
32.84  
AI-driven Assessments × Motivation  
200  
4
0.000  
Significant (Reject  
)
The calculated Chi-square ( ) value of 32.84 exceeded the critical value of 9.49 at 4 degrees of freedom and  
0.05 level of significance. Therefore, the null hypothesis was rejected, and the alternative hypothesis was  
accepted. This finding indicates that AI-based assessments significantly enhance students' motivation in  
learning by making evaluation more interactive and reducing test anxiety. From the perspective of Cognitive  
Load Theory, adaptive AI assessments optimize cognitive load by presenting challenges appropriate to  
learners' current knowledge levels, thereby maintaining optimal motivation.  
Hypothesis Two: : There is no significant relationship between AI-based feedback and student engagement.  
This hypothesis was tested using an Independent Samples t-test, comparing mean engagement scores of  
students with high versus low exposure to AI feedback systems (automated tutoring chatbots and instant  
grading platforms).  
Table 4: t-test Results for AI-based Feedback and Student Engagement  
Group  
N
Mean SD t-cal df  
p-value Decision  
0.65 4.62 198 0.000  
0.81  
High AI-feedback Group 100  
Low AI-feedback Group 100  
4.12  
3.72  
Significant (Reject  
)
The calculated t-value of 4.62 exceeded the critical value of 1.96 at 198 degrees of freedom and 0.05 level of  
significance. Thus, the null hypothesis was rejected, and the alternative hypothesis was accepted. This finding  
demonstrates that AI feedback significantly enhances students' participation and engagement in learning tasks.  
According to the Engagement Framework, engagement comprises behavioral, emotional, and cognitive  
components that are strengthened by relevant and timely feedback.  
Hypothesis Three: : There are no effective strategies for integrating AI in assessments without undermining  
motivation and creativity.  
Responses related to proposed integration strategies (teacher training on AI tools, balanced AI-human  
assessment models, ethical safeguards, and creative assessment design) were analyzed using descriptive mean  
analysis and Chi-square test.  
Table 5: Chi-square Test Results for AI Integration Strategies  
Variable  
N
df  
p-value Decision  
-cal  
AI Integration Strategies × Motivation/Creativity 200 4 29.45 0.001  
Significant (Reject  
)
The calculated Chi-square ( ) value of 29.45 exceeded the critical value of 9.49 at 4 degrees of freedom and  
0.05 level of significance. Therefore, the null hypothesis was rejected, and the alternative hypothesis was  
accepted. This finding indicates that effective strategies exist for integrating AI into assessments without  
undermining motivation and creativity.  
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DISCUSSION  
The analysis revealed a significant effect of AI-driven assessments on students' motivation. Students who  
participated in AI-assisted assessments using adaptive platforms demonstrated higher levels of interest and  
enthusiasm than those assessed through traditional methods. From the lens of Self-Determination Theory (Deci  
& Ryan, 2000), AI assessments enhance intrinsic motivation by satisfying three psychological needs:  
autonomy (self-paced learning), competence (appropriately challenging tasks), and relatedness (interactive  
feedback). Furthermore, Cognitive Evaluation Theory suggests that immediate, constructive feedback  
strengthens learners' perceived competence, thereby increasing intrinsic motivation. This finding agrees with  
Hattie and Timperley (2007), who reported that immediate and constructive feedback strengthens learners'  
motivation and confidence. Similarly, Luckin et al. (2016) observed that adaptive AI systems that adjust test  
item difficulty to match learner ability sustain interest and prevent frustration. The result suggests that AI-  
driven assessments have the potential to transform the learning environment into a more engaging, supportive,  
and motivating experience for students.  
The analysis demonstrated a significant relationship between AI-based feedback and student engagement.  
Students who received continuous, personalized feedback through AI platforms (such as intelligent tutoring  
systems and automated grading tools) were more actively involved in learning activities than those who did  
not. According to Fredricks, Blumenfeld, and Paris's (2004) multidimensional engagement framework,  
engagement encompasses behavioral, emotional, and cognitive aspects. AI feedback systems strengthen all  
three dimensions: behavioral engagement through increased participation in learning tasks, emotional  
engagement through sustained interest and reduced anxiety, and cognitive engagement through deeper  
information processing and metacognitive reflection. Lee and Hammer (2011) found that gamified AI systems  
increase students' persistence, enjoyment, and participation. The interactive nature of AI feedback helps  
learners monitor their progress in real-time, making learning more dynamic and fostering continuous  
engagement in academic tasks. This supports Flow Theory (Csikszentmihalyi, 1990), which posits that optimal  
engagement occurs when challenge levels match skill levels—a balance that adaptive AI systems can maintain  
effectively.  
The analysis revealed that effective strategies exist for integrating AI into educational assessments without  
negatively affecting students' motivation and creativity. With appropriate ethical frameworks, teacher  
involvement, and balanced use of AI technologies, assessment systems can become both innovative and  
human-centered. Drawing on the TPACK framework (Technological Pedagogical Content Knowledge),  
successful AI integration requires educators to possess not only technological skills but also pedagogical  
knowledge to implement AI tools in ways that enhance rather than constrain learning. The blended assessment  
model, which combines AI efficiency with human judgment, preserves the creative and critical thinking  
elements that purely automated systems might overlook. This finding aligns with UNESCO (2021), which  
recommended that AI technologies should be integrated into education in ways that uphold ethical standards  
and support inclusive, learner-centered practices. Similarly, Selwyn (2019) argued that while AI can increase  
assessment efficiency and fairness, it should complement rather than replace the role of teachers in nurturing  
creativity and critical thinking. From a constructivist perspective, effective AI integration must support active  
knowledge construction rather than passive information consumption, ensuring that learning remains both  
motivational and authentic.  
CONCLUSION  
Artificial Intelligence is reshaping educational assessments by promoting adaptive, efficient, and personalized  
evaluation systems. It has the potential to significantly boost motivation and engagement by reducing anxiety,  
offering real-time feedback, and sustaining learner interest. However, challenges such as bias, overreliance,  
and reduced human interaction must be addressed. A balanced approach that combines AI with human  
judgment and ethical safeguards will ensure assessments are both innovative and meaningful.  
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RECOMMENDATIONS  
1. Government and policymakers should integrate AI-driven assessments into national education  
frameworks while ensuring ethical safeguards.  
2. Teachers should combine AI feedback with human guidance to maintain personalized and meaningful  
evaluation.  
3. Schools should organize digital literacy and AI ethics programs to help students engage responsibly  
with assessment technologies.  
4. Further research should explore the long-term motivational outcomes of AI-based assessments in  
Nigerian schools.  
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