With increasing class sizes and heightened demands on faculty time, providing timely feedback has become a critical challenge for student retention, as nearly 25% of full-time college students fail to re-enroll after their first year (Integrated Postsecondary Education Data System, 2023). This paper explores how artificial intelligence can be employed as a tool to manage the non-content specific tasks of grading, such as checking parameters, word counts, paragraphing, and citation formatting (APA). By automating these routine tasks, educators can educator can provide "extremely timely" feedback, which quantitative analysis reveals is a significant determinant of student motivation and satisfaction (Olsen & Hunnes, 2024). Research indicates that rapid feedback—ideally within 24 to 48 hours— allows students to correct misconceptions while the material is fresh, significantly boosting engagement and reducing dropout rates tied to a lack of informational transparency (Hattie, 2023; SchoolAI, 2025). This intervention is particularly vital after midterms, where early feedback acts as a "mid-quarter" pivot point to foster an inclusive environment and provide students the agency to modify their learning strategies (Messier, 2022). Ultimately, integrating AI responsibly handles the mechanical "lift" of grading, allowing faculty to focus on high-level, personalized feedback that supports both instructional efficiency and student learning progression.
Implementation steps and strategic initiatives
Dr. Angelette Prichett's work at Harris-Stowe State University on AI integration for student success and social work education calls for a multi-pronged action plan that addresses both the technological and human dimensions of AI adoption. The first step is to establish a Student Success AI Task Force comprising faculty from social work, counseling, and student affairs, along with student representatives and community partners. This task force should be charged with developing an institution-wide framework for using AI tools to support student retention, mental health, and academic success, with particular attention to the needs of students from underserved communities.
AI-powered early alert systems should be piloted in collaboration with the institution's advising and student affairs offices. These systems should analyze LMS engagement data, grade trends, and attendance patterns to identify students at risk of academic difficulty or disengagement before problems become critical. Advisors and faculty should receive training on how to interpret and act on early alert notifications, with clear protocols for follow-up outreach and referral to support services. Student privacy and data ethics must be central to the design and governance of any AI-powered alert system.
In social work courses specifically, AI tools should be integrated as case study generators, simulation partners, and research assistants that help students develop the analytical and empathetic skills required for professional practice. Role-play simulations using AI chatbots can provide students with low-stakes practice in client interviewing, crisis intervention, and motivational interviewing techniques. Faculty should debrief these simulations carefully, helping students identify the limitations of AI as a proxy for human interaction and the ethical considerations that arise when AI is used in social service contexts.
Community partnerships with social service agencies, mental health providers, and government offices should be cultivated to provide students with applied AI projects that address real community needs. These partnerships could take the form of sponsored capstone projects, practicum placements at agencies that use AI tools, or co-curricular service-learning projects. Such connections strengthen students' career readiness and demonstrate the practical value of AI competency in the social work profession.