AI-Based Assessment and Evaluation

Description

Concept by Cecilia Iris Baldo

In today’s educational landscape, teachers encounter the pressing challenge of assessing and evaluating student learning amidst the rapid integration of technologies, notably Artificial Intelligence (AI).

This necessitates a deep understanding of the diverse evaluation methods available, alongside the ability to seamlessly incorporate AI tools into these practices.

This course seeks to empower educators by revisiting their understanding of assessment and evaluation practices in light of the AI era.

Participants will engage with the realities of AI and engage in reflective discussions on various assessment methods. Ultimately, they will collaboratively design guidelines tailored to meet the evolving demands of contemporary education.

During the course, participants will explore a range of assessment and evaluation methods, including traditional approaches and those enhanced by AI technologies.

Through interactive activities and discussions, educators will analyze the strengths and limitations of each method and consider how they can be adapted to optimize student learning outcomes in the AI era.

By the conclusion of the course, participants will emerge equipped with a comprehensive toolkit of assessment and evaluation strategies tailored to the demands of contemporary education.

With a nuanced understanding of both traditional and AI-enhanced methods, educators will be well-prepared to navigate the complexities of student assessment in the digital age.

Learning outcomes

The course will help the participants to:

• Gain a comprehensive understanding of traditional assessment and evaluation practices;
• Explore the role of Artificial Intelligence (AI) in modern educational assessment;
• Analyze the benefits and limitations of AI-enhanced assessment methods;
• Develop strategies for integrating AI tools into assessment and evaluation practices;
• Collaboratively design guidelines for adapting assessment practices to the AI era;
• Apply newfound knowledge and skills to enhance student learning outcomes in their educational context.

Tentative schedule

Day 1 – Course introduction

• Introduction to the course, the school, and the external week activities;
• Icebreaker activities;
• Presentations of the participants’ schools;
• Assessment and evaluation;
• AI: the basics and the current scenario.

Day 2 – Evaluation

• Reflection on the current methods of evaluation and how they reflect our system;
• Theory-based, quantitative, and qualitative evaluation methods;
• Formative and summative evaluation

Day 3 – Assessment

• Project-based assessment;
• Self-assessment and peer-assessment;
• Continuous assessment.

Day 4 – Finding solutions

• Adaptative assessments;
• How to adapt our assessment and evaluation methods to the AI scenario;
• Case studies around the world.

Day 5 – Practice

• Prepare your own evaluation and assessment guidelines

Day 6 – Course closure and cultural activities

• Course evaluation: round-up of acquired competencies, feedback, and discussion;
• Awarding of the course Certificate of Attendance;
• Excursion and other external cultural activities.