The chapter explores the transformative potential of artificial intelligence (AI) in reshaping assessment, grading, and feedback processes in higher education. They cover real-time feedback mechanisms, AI-driven practices, and evaluation of AI-based assessments, promoting a more equitable, student-centered learning environment. AI is revolutionizing higher education by providing personalized grading criteria, analyzing student data, and adjusting assessment criteria to accommodate diverse learning styles. This approach promotes student engagement, fairness, and equity, enabling educators to tailor teaching strategies and address learning gaps. The chapter emphasizes faculty training and AI-driven enhanced methods.
TopArtificial Intelligence (AI) refers to computer systems that can perform complex tasks traditionally performed by humans, such as reasoning, decision-making, and problem-solving (Schuett, 2019). AI in Education (AIED) is revolutionizing education by streamlining assessment processes, enhancing learning outcomes, and creating personalized experiences. Originating in the 1970s, AIED aims to foster student success and drive innovation in teaching and learning (Humble & Mozelius, 2022; Rosen, 2023). AI is revolutionizing higher education by reshaping traditional assessment methods, empowering students, and enhancing efficiency. Technology-enhanced language learning (TELL) educators have been using various educational technologies for three decades (Zou et al., 2021). Artificial Intelligence (AI) is anticipated to emerge as a very useful technological advancement in the next years, alongside other transformative technologies such as robots, virtual reality, 3D printing, and networks (Hooda et al., 2022). AI can help educators manage resource requirements for assessments, grading, and feedback, transforming resource management with growing class sizes and diverse student groups.
AI integration can enhance critical thinking, promote ethical considerations, and equip students with necessary competencies for professional AI implementation (De Gagne, 2023). Next-generation educational technology, including artificial intelligence, has led to widespread use of computers and information and communication technologies in education (Ouyang et al., 2023a; Zawacki-Richter et al., 2019). AI assessment systems enhance educational experiences by identifying targeted interventions ensuring academic excellence. However, their implementation faces ethical constraints, despite rapid growth in education and research (Humble & Mozelius, 2022). Ali's (2020) study explores AI integration in language teaching, focusing on Automatic Speech Recognition (ASR) technology. Chatbots use keyword-matching to evaluate students' spoken language proficiency, enhancing communication between humans and machines (Ali, 2020). AI integration in flipped classrooms improves academic performance and student enthusiasm, fostering positive attitudes towards language acquisition among scholars (Ali, 2020).
In their study, Zawacki-Richter et al. (2019) conducted a comprehensive analysis of AI applications in the field of higher education. Their research spanned the years 2007 to 2019 and included a worldwide perspective (Zawacki-Richter et al., 2019). The findings revealed that the prominent areas of AI-enhanced education included profiling and prediction, assessment and evaluation, adaptive systems and customisation, as well as intelligent tutoring systems (ITS) (Zawacki-Richter et al., 2019). AI-powered systems can assess and grade the essays through analysing the structure, content, grammar and overall quality of the writing. However, these systems use NLP and machine learning in providing the detailed feedback to the students.
Automated essay scoring (AES), developed since 1966, assigns scores to student responses based on grammar, diction, structure, and other characteristics. Despite challenges, researchers continue to work on resilient AES systems (Ramesh & Sanampudi, 2022). The Transformer-based approach for Automatic Encoding System (AES) offers advantages over Bag-of-Words (BOW) in knowledge examinations and other tasks. Future research should explore the benefits of hierarchical coherence modeling and transformer-based language models, as well as the potential for transitioning between different models to enhance human evaluations and score “very easy” texts (Ludwig et al., 2021).