How should universities rethink teaching and assessment in the AI era?

Using AI to enhance teaching and learning

One of AI's most significant opportunities lies in its ability to support more personalised learning experiences. However, institutions need to be deliberate about where technology adds genuine educational value.

At Laureate Mexico, experimentation is already underway through AI tutors, coaching tools and adaptive learning experiences. Its Lexio platform supports personalised content delivery, adaptive learning pathways and Socratic-style dialogue, while subject-specific AI tutors have been introduced across parts of its course portfolio.

For Caraballo, the key distinction is between removing barriers to learning and removing the learning process itself: "It's about reducing the friction that gets in the way of learning and not reducing the friction that helps you learn."

AI can help students locate information more quickly or identify gaps in their understanding. However, activities such as solving complex problems, building arguments and evaluating evidence often require productive struggle, which remains a critical part of learning.

The goal, therefore, is to use AI strategically so that students and educators can spend more time focusing on meaningful learning.

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Rethinking assessment in the age of AI

As universities review their assessment strategies, the starting point should be learning outcomes rather than technological capabilities.

Research shared during the webinar found that more than half of surveyed students want clear and consistent guidance on when and how AI can be used in assessment. Students were also more supportive of education around ethical AI use than the use of AI detection tools.

For Caraballo, the bigger question is whether universities are accurately measuring student learning in the first place:

"Our North Star should be how effectively we evaluate the learning of the student."

If AI can generate essays, write code and solve routine problems, institutions need to reconsider what those outputs actually reveal about a student's understanding and capabilities.

Elliott highlighted approaches such as capstone projects, where students demonstrate how they can apply knowledge to real-world problems. Universities may also place greater emphasis on the process behind an assessment, including how students formulate questions, evaluate AI-generated content and apply disciplinary expertise.

AI may also help institutions provide more personalised feedback at scale. However, technology should always support educational objectives rather than define them. As Caraballo summarised: "pedagogy before technology."

Preparing graduates for an AI-enabled workforce

Preparing students for an AI-enabled workplace involves far more than teaching them how to use a particular tool.

Alongside AI fluency and subject knowledge, Caraballo emphasised the importance of developing durable skills, including critical thinking, curiosity and adaptability. These capabilities are likely to remain valuable even as AI technologies continue to evolve.

Students also need to understand how AI is transforming their chosen professions. At Laureate Mexico, this has informed an approach described as AI for special purposes, which focuses on the AI applications and capabilities most relevant to specific disciplines and career pathways.

As a result, universities may need to review and update curricula more frequently to ensure programmes remain aligned with technological change and workforce needs.

Building AI-ready universities

Throughout the discussion, speakers returned to a common theme: ,success should not be measured by the number of AI tools an institution adopts. Instead, universities should focus on whether students are developing stronger capabilities, whether faculty are better equipped to support learning and whether assessment continues to provide meaningful evidence of student achievement.

That makes AI adoption as much a people and pedagogy challenge as a technology challenge. Faculty development, institutional culture and curriculum design are just as important as the tools themselves.

Rather than simply embedding AI into existing curricula, universities have an opportunity to rethink how learning is designed, assessedand improved in an era where AI is reshaping both education and the workplace.