3. Principles of Course Design with AI

3.1 Integrating AI into the Constructive Alignment Approach

Constructive alignment is an effective technique to create pedagogically sound courses. It means systematically aligning the core elements of teaching - learning outcomes, learning activities, and assessment - with one another. When integrating AI into a course, it is crucial to think about the purpose of AI and how students can best utilize it to achieve the intended learning outcomes.

Teachers are recommended to follow three key steps in course design:

1. Define the AI-related elements of learning outcomes in terms of assessable knowledge, skills, and competencies. This is the basis for selecting course content.

2. Establish the criteria and methods for assessing the achievement of AI-related learning outcomes.

3. Select appropriate AI tools and determine how students will use them in exercises, assignments, presentations, examinations, and other activities.

When applying the constructive alignment approach to course design, it is helpful to distinguish between different kinds of AI-related learning outcomes:

• AI knowledge and skills may be defined as direct learning outcomes, e.g. when students are expected to describe the components of AI systems, to explain how they are connected, or to develop an AI system.

  In this case, AI knowledge and skills are subject-specific, and the learning outcomes focus on professional, methodical, or analytical competencies.

• Students might be advised to use AI as a tool that helps them achieve an overarching learning outcome, e.g. using a specific AI tool to summarize texts and to extract relevant content to produce an overview of the state of the art in a given subject.

  In the second case, a variety of AI tools can be used to help achieve a higher-order learning outcome. In relation to this learning outcome, the use of AI can be considered an important element of general AI literacy, and therefore a transversal competence.

This distinction is important because it influences both the emphasis placed on each learning activity during assessment and the criteria used to evaluate it.

3.2 Cultivating critical thinking

Critical thinking essentially means the ability to interpret, analyse, and evaluate information, facts, and behaviour independently, reflexively, and sceptically. This means being able to make a ‘purposeful, self-regulatory judgment which results in interpretation, analysis, evaluation, and inference, as well as explanation of the evidential, conceptual, methodological, criteriological, or contextual considerations upon which that judgment is based’ (Facione 1990: 2).

AI tools generally generate incomplete and often incorrect, distorted, or misleading outputs, the ability to critically scrutinize AI-generated outputs is gaining particular importance. Therefore, teachers need to advise and train students to critically assess AI outputs, verify facts, and recognize biases, preparing them to discern accurate information. See Chapter ‘4.4 Teaching strategies to promote critical thinking’ for practical ideas to support students’ critical thinking.

3.3 Fostering academic integrity

With generative AI tools capable of generating text, images, sound, and code, academic institutions need to re-emphasize the relevance of the independent work of students, teachers, and researchers. This shift necessitates the development of academic integrity policies focusing on disclosing AI use, distinguishing between human and AI-generated content through proper documentation, quotation, and acknowledgment of outputs generated by AI as basic requirements.

A promising approach towards ensuring the transparent use of AI is to require teachers, students, and researchers to document their use of AI and to explain how, and to what extent, AI-assisted or AI-generated data and output were used in preparing assignments, theses, or research reports.

For handling academic integrity in the course, see Chapter ‘4.7 Demanding academic integrity and good scientific practice’.