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In this minor, BUas students from other BUas programmes than ADS&AI  collaborate with ADS&AI students to find solutions of real-life problems of the industry you study for using data and artificial intelligence applications. ADS&AI students serve as the technological experts, you, together with other students from your programme, serve as the domain experts. Together you analyse the problem, assess solutions used in the past, describe data needed to contribute to the solution, develop the solution and present the outcomes to your client.

Examples of potential projects are:

• Diminishing returns of web shops by designing smarter recommender systems;
• Findings smart solutions for last mile distribution;
• The use of generative AI in games production;
• The use of robotics in hospitality;
• Increasing advertising revenue’s in media by using data in content development;
• Energy saving by analysing data in smart buildings;
• The use of data to improve the impact of social media campaigns.
• The use of generative AI to improve a tourism policy learning game (scithos and SmartCulTour)
• Curating Geo and Cultural data for a location-based game Curating GeoData for Cultural Heritage Route St Maarten.pptx
• Explore the roles of digital virtual intelligent agents in making citywide intangible and tangible cultural heritage accessible for citizens, visitors and tourists.
• Create visualised virtual agent/s that represent hidden cultural actors through which users can discuss heritage sites – either as a ‘tour guide’ or a ‘character’ associated with a specific local place
• AI application in services experiences of museums and cultural institution
• Automation of customized travel: Users input their destinations, number of travelers and travel preferences. Then they can get a “tailor-made” travel plan for a destination
• using AI in roots tourism, to help reconstruct a network of relationships and family histories, through memory artifacts like photographs, letters, or other family documents or via places that are now abandoned
• AI applications in enhancing cultural, societal, sustainable and the generic customer journey. How can these be implemented into policies and strategies for creating a well-balanced approach to stimulate the tourism industry?
• How can AI serve to promote Equality, Diversity and Inclusivity and enhance the experiential journey of the 21st- century tourist, and simultaneously assist in creating a sustainable tourism industry? 
• AI applied to the management of the tourism company/DMO sustainability. Use AI (fed with bid data, scientific principles and policy requirements) to draft fit for purpose and fir for tourism company (DMO)  sustainability strategies; with the help of AI to critically assess to which extent those are realistic within company/DMO context; to propose realistic action plans and business models to run sustainability transitions and to develop monitoring/agile strategy adaptation mechanisms (development of the decision support tools for tourism companies and DMOs).
• AI applied to identification of sustainability pathways versus greenwashing claims within tourism sector (very actual in the framework of the discussions on the stricter regulation for the sustainability greenwashing). 
• How to use AI in tourism to better inform (advanced personalisation) and nudge travellers (iteractive crowdsourcing procedures or any other) at the earlier stages of their choices, to more sustainable tourism travel choices. This moment is – when tourists only start brainstorming/investigate/browse holiday ideas- before booking any travel mode; accommodation; experiences; etc – help of AI in understanding how to capture that moment and how efficiently nudge tourists into more sustainable tourism travel choice ( we can try to facilitate the contacts with Google for this assignment).
• How to use AI in changing existing tourism travel narrative of “further is better” and “faster is better”: this narrative forming starts from the children books; largely communicated via the marketing/publicity; press and policy discourses; social media, etc. Help of AI to identify and analyse all the channels forming this narrative and development of pathways to change it.
• ABEL/AT/AI minor: tourism traffic flow management/policy integration within mobility/logistics policy. At this moment mobility, logistics transport and tourism mobility policy making/target setting are not integrated within each other. AI can help on the development and improvement of the integral policy, targeting emissions reduction (e.g. analysing effects of the modal shift in tourism transport and providing more insight into the interaction between passenger, freight transport, tourism travel and infrastructure). This assignment closely relates to NLAIC and is one of its priorities in relation to mobility/logistics.

In this minor you will embark on a transformative journey, where the art of communication meets the science of data. This program is designed for BUas students poised to become the next generation of 'Data Translators'—visionary leaders who bridge diverse fields with the technological frontier of Artificial Intelligence and Data Science.

As a participant, you will:

• Engage in Cross-Disciplinary Collaboration: Unite with ADS&AI technologists to tackle real-world industry challenges through the lens of your domain expertise.
• Develop as a Domain Translator: Learn the essential skill of articulating complex industry problems into data-centric language that AI experts can navigate and innovate upon.
• Innovate with Impact: Generate cutting-edge solutions by synthesizing past approaches, current data insights, and AI applications to elevate your field.
• Deliver Transformational Solutions: Craft and present revolutionary, data-driven outcomes to industry clients, steering your sector toward a smarter, more efficient future.
• Through hands-on projects, you will not only contribute domain knowledge but also become fluent in the strategies involving data analysis and AI application. This minor is not just about finding answers—it’s about pioneering them.

Projects could include:

• Revolutionizing E-commerce: Design advanced recommender systems to minimize returns and enhance the shopping experience.
• Innovating Distribution: Develop intelligent solutions for 'last mile' delivery challenges.
• Advancing Game Production: Integrate generative AI to push the boundaries of creativity and personalization.
• Transforming Hospitality: Employ robotics to revolutionize customer service and operational efficiency.
• Elevating Media Revenues: Utilize data-driven content strategies to amplify advertising impact.
• Optimizing Energy Efficiency: Analyze smart building data to significantly reduce carbon footprints.
• Enhancing Social Engagement: Leverage analytics to maximize the influence and reach of social media initiatives.

At least 90 credits in the post-propaedeutic stage at the start of the project. No technological knowledge required, just your domain knowledge.

All materials will be distributed via the Digital Learning Environment of ADS&AI (Brightspace)

Individual contribution to the project will be recorded in a worklog and a learning log. This individual contribution will be assessed via a rubric that is being presented at the beginning of the project.

Topics

Foundations of Artificial Intelligence

• Comprehensive introduction to AI concepts, history, and applications.
• Exploration of various AI disciplines: Machine Learning, Deep Learning, Neural Networks, NLP, and Robotics.

Ethics, Law, and Data Governance

• In-depth discussion on data privacy, intellectual property, and legal frameworks.
• Ethical considerations in AI: bias, fairness, transparency, and accountability.
• Global and regional data protection regulations (e.g., GDPR, HIPAA).

Research Methods and Problem-Solving

• Strategies for identifying research questions in AI and data science.
• Qualitative and quantitative research methods.
• Problem-solving frameworks and critical thinking skills for 'wicked problems'.

Communication and Data Storytelling

• Techniques for effective data visualization and presentation.
• The art of storytelling with data to influence decision-making.
• Communication skills tailored for diverse audiences, including technical and non-technical stakeholders.

Driving Change in Organizations

• Theories and models of change management in the context of technological innovation.
• Case studies on organizational adaptation to AI-driven workflows.
• Strategies for overcoming resistance and managing stakeholder expectations.

Project Management in Tech-Driven Environments

• Fundamentals of project management: planning, execution, monitoring, and closure.
• Agile methodologies and their application in AI projects.
• Collaboration tools and techniques for managing interdisciplinary teams.

Structure of minor
The ADS&AI programme works with a project-based approach, supported by an extensive digital learning environment, which students use to acquire the knowledge and skills used. During lab days students work in groups of app. 6 (50% domain experts, 50% data specialists on a real life project, for a real life client under supervision of experienced staff.