Which of the following are correct in the context of Artificial Intelligence (AI) as one of the innovations in the evaluation system?
A. It is a software system used for automated scoring.
B. ‘AI’ technique is applied to learn how an instructor grades a problem/response.
C. It is the final authority about the student's performance.
D. The instructor evaluates a sample set of students' responses and the ‘AI’ system creates a computer model incorporating rules inferred.
E. Presently, most of the higher educational institutions in the country employ ‘AI’-based evaluation system.
Choose the most appropriate answer from the options given below:

Correct option is D

Artificial Intelligence (AI) in the evaluation system involves automated scoring, learning how instructors grade responses, and creating rules through sample evaluation, making these statements valid. Statement (C) and (E) are incorrect as they misrepresent the role of AI and its current adoption.
Information Booster
1. Statement Analysis:
Statement (A): Correct. AI is widely used in automated scoring systems, such as essay scoring, multiple-choice evaluations, and performance assessments.
Statement (B): Correct. AI models use machine learning to analyze how instructors grade responses and apply the same logic for automated grading.
Statement (D): Correct. AI systems are often trained on a dataset of instructor-graded responses to infer rules and patterns for future evaluations.
2. Incorrect Statements:
Statement (C): Incorrect. AI is not the final authority on a student’s performance; human oversight is often required for critical or nuanced evaluations.
Statement (E): Incorrect. While AI adoption is increasing in educational institutions, it is not yet prevalent across "most" higher education systems.
3. Applications of AI in Education:
· Automated grading for objective and essay-based assessments.
· Personalized feedback systems that analyze students' responses.
· Evaluation of performance trends to assist instructors.
Additional Knowledge
· AI techniques, such as Natural Language Processing (NLP), are used for text-based response evaluation.
· Human-AI collaboration ensures accuracy and fairness in grading.
· Challenges in AI-based evaluation include bias in training data and limitations in understanding creative or subjective responses.