Evaluation of AI-Based Automated Assignment Submission and Feedback Systems in Federal University, Lokoja, Kogi State
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Background of the Study
Assignment submission and feedback processes are essential components of academic assessment in universities, influencing student performance and engagement. Traditional methods of submitting assignments, typically through physical papers or email, can lead to inefficiencies in tracking, grading, and providing timely feedback (Sharma & Singh, 2023). Additionally, the large volume of assignments in modern universities makes it increasingly challenging for lecturers to manage, grade, and offer personalized feedback. AI-based systems offer solutions to automate the submission, grading, and feedback processes, enhancing efficiency and timeliness (Zhou et al., 2024).
Federal University, Lokoja, Kogi State, with its growing student population, can benefit from the implementation of AI-based automated assignment submission and feedback systems. Such a system would streamline assignment handling, reduce administrative burden on lecturers, and ensure students receive timely, personalized feedback on their work (Adewale & Alabi, 2024). This study aims to evaluate the effectiveness and efficiency of implementing AI-powered systems for assignment submission and feedback at the university.
Statement of the Problem
The current manual processes of assignment submission, grading, and feedback at Federal University, Lokoja are time-consuming and prone to delays, which impacts the quality of feedback students receive. The lack of automation in these processes creates administrative bottlenecks and limits the ability to offer personalized, real-time feedback. The study will explore the feasibility and impact of AI-based systems in addressing these issues by providing a faster, more efficient, and personalized approach to managing assignments.
Objectives of the Study
- To design and implement an AI-based automated assignment submission and feedback system for Federal University, Lokoja.
- To evaluate the efficiency of the AI-based system in processing assignment submissions and providing feedback.
- To assess the impact of AI-based feedback on student performance and satisfaction.
Research Questions
- How effective is the AI-based automated assignment submission system in improving the speed and efficiency of assignment processing?
- What is the quality of feedback provided by the AI system in comparison to traditional methods?
- How does the implementation of AI-based assignment submission and feedback systems affect student engagement and performance?
Research Hypotheses
- The AI-based system will significantly reduce the time required for assignment submission and feedback delivery.
- Students receiving AI-generated feedback will report higher levels of satisfaction with the feedback compared to traditional methods.
- The implementation of the AI-based system will lead to improved academic performance among students.
Significance of the Study
The study will offer a solution to the challenges faced by Federal University, Lokoja in managing assignments and feedback. By implementing an AI-based system, the university can improve efficiency, ensure timely feedback, and enhance the overall student learning experience.
Scope and Limitations of the Study
This study will focus on the design, implementation, and evaluation of an AI-based assignment submission and feedback system for undergraduate students at Federal University, Lokoja. The study will be limited to the university's student body and will not extend to postgraduate students or other institutions.
Definitions of Terms
AI-Based Automated Assignment Submission System: A digital system powered by artificial intelligence that automates the process of submitting, grading, and providing feedback on assignments.
Assignment Feedback: The evaluation and response provided by instructors on students’ assignments, including areas of improvement and strengths.
Student Engagement: The level of involvement and interaction students have with their academic activities and feedback.
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