Innovate for Impact

Learning outcomes

1. Imagine creative marketing opportunities with appeal to a target audience
2. Define a complex marketing problem in an international business setting
3. Develop a SMART method to approach complex marketing problems within a given timeframe
4. Integrate insights from academic and professional sources in the analysis of complex marketing problems
5. Design sustainable solutions to complex marketing problems by using the intervention cycle
6. Demonstrate an evidence-based line of reasoning using primary and secondary data
7. Use AI in the design and assessment of marketing solutions

Assessment

AI usage was part of the assessment through rubric-based criteria. Students were expected to use at least two AI tools, document their interactions, and reflect critically on how AI contributed to creativity, research, and design.

Evaluation

The impact was evaluated through student AI usage logs, instructor reflections, feedback during coaching sessions, and review of student outputs. The course also planned pre- and post-course AI literacy surveys by the AI-HED team.

• KPIs tracked: Yes — Suggested indicators included student performance, engagement, quality of AI reflections, the total number of students reporting AI as helpful, and the number of AI-generated ideas that were used or revised. A post-course survey (N ≈ 37–39) produced an overall course evaluation of 3.85 / 5. Highlights: 'helped me use AI effectively in the course topic(s)' (4.10), 'helped me better understand complex concepts' (4.11), 'helped me apply knowledge in practical ways' (4.08), and 'helped me develop relevant skills for my future' (4.03). Lower scores were seen on teacher guidance items, e.g. 'the teacher guided us in using AI in a responsible way, following ethical considerations and privacy rules' (3.41).
• Formal institutional evaluation: Yes

Risk management

The main risks identified were overreliance on AI, uneven AI literacy, bias in outputs, superficial reflection, and technical or access issues. These were managed through lecturer guidance, peer support, structured prompting exercises, reflection tasks, and clear expectations that AI should support rather than replace student thinking. Students were encouraged to validate outputs and compare them with theory, evidence, and their own judgment.

Challenges

Uneven student experience with AI and differing skill levels

Addressed through demonstrations, in-class coaching, and peer learning

Overreliance on AI outputs

Managed by encouraging students to use AI as a co-creator rather than a ready-made answer machine, supported by reflection logs

Limited free access to some tools and occasional technical issues

Mixed free/paid access within teams, with lecturer guidance to work around limitations

Difficulty judging output quality; some students felt overloaded by the number of available tools

Reflection tasks and in-class coaching helped students evaluate AI outputs more critically

Scalability

This experience can be scaled to other courses, especially project-based courses where students need support in ideation, research, analysis, and communication. To scale it well, lecturers would need basic training in AI tools and prompting, clear assessment criteria, and practical guidelines for responsible use.