Bung na Lainim  (Online Meet #3)

The POSITIVE implications of AI in student assessment:

1. Enhanced objectivity and fairness: AI-powered assessment methods can provide objective and consistent evaluations, reducing subjective biases and promoting fairness in student assessment.

2. Personalized learning experiences: AI can analyze data on students' strengths, weaknesses, and learning styles to provide personalized feedback and tailored interventions, helping students improve their performance and achieve better learning outcomes.

3. Increased efficiency and effectiveness: AI can automate time-consuming tasks such as grading and data analysis, freeing up educators' time and resources, allowing them to provide timely feedback, and facilitating a more efficient and effective assessment process.

The NEGATIVE  implications of AI in student assessment:

1. Potential for bias and discrimination: AI systems used in student assessment may inadvertently introduce biases, such as gender, race, or socio-economic bias, if not properly designed and validated. This can result in unfair assessment outcomes and contribute to educational inequity.

2. Overreliance on technology: Over reliance on AI in student assessment may lead to a reduction in human involvement and judgment, potentially diminishing the importance of qualitative factors, such as critical thinking, creativity, and subjective evaluation, in the assessment process.

3. Ethical concerns: The use of AI in student assessment raises ethical concerns, including issues related to data privacy, security, and transparency. There may be concerns about the collection, storage, and use of students' data, as well as the potential for unintended consequences and misuse of AI technology in student assessment.