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In this systematic review, we synthesize ten empirical peer-reviewed articles published between 2019 and 2023 that used generative artificial intelligence (GenAI) for automated feedback in higher education. There are significant opportunities and challenges to integrate these tools effectively into learning environments as the demand for timely and personalized feedback grows. We examine the articles based on instructional contexts and system characteristics, identifying critical implementation possibilities for GenAI in automated feedback. Our findings reveal that GenAI provides diverse feedback across various contexts with multiple instructional purposes. GenAI systems can reduce instructor workload by automating routine grading and feedback tasks, allowing educators to focus on more complex teaching responsibilities with augmented capabilities. Additionally, these systems enhance communication, offer cognitive and emotional support, and improve accessibility by creating supportive, stress-free learning environments. Overall, implementing GenAI automated feedback systems improves educational outcomes and creates a more efficient and supportive learning environment for students and instructors. We conclude with future research directions to better integrate GenAI with human instruction by reconsidering instructors’ roles, especially in providing feedback to create more effective educational experiences.
Lee et al. (Sun,) studied this question.