7B002E - AI Innovation Lab 01 Sep 2026 - 31 Jul 2032 | Version 0

Associated Module Information

Module Code: 7B002E
Module Title: AI Innovation Lab
Faculty: Faculty of Business and Creative Industries
Faculty Group: Global Governance
Faculty Sub Group: Global Governance
Module Leader: Michael Parsons
Module Team: Adeyemi Aromolaran
First Intended Intake: SEP 2026 Final Year of Intake:
Date Closed:
Credit Value: 30 Credit Level: 7
Language: English
Percentage of Module Taught in Welsh: 0
Equivalent Module:
HECOS codes: 100078 - business and management
HECOS Code Weighting: 100

Document Version Information

Version 0
Valid From 01 Sep 2026
Valid To 31 Jul 2032

Module Aims

  1. To critically evaluate the strategic, ethical, and pedagogical implications of AI-enabled innovation in higher education, demonstrating commercial awareness and alignment with the demands of Digital Transformation and AI-driven economies. 

 

  1. To synthesise interdisciplinary knowledge and apply creative problem-solving to collaboratively design and present an ethical AI prototype that addresses a real-world strategic challenge in educational innovation. 

 

  1. To foster professional autonomy and reflective practice through the development of a personalised training portfolio and critical evaluation of the project lifecycle using advanced meta-cognitive tools. 

Content Summary

The capstone module, AI Innovation Lab, will position students as FBCI consultants to explore the transformative potential of AI in shaping educational innovation in higher education. Students will investigate how AI can enhance learning, collaboration, and strategic thinking in university settings, drawing on insights from across the curriculum. Through challenge-based projects, students will design innovative and ethical AI-enabled solutions, such as personal learning agents or collaborative platforms linking academia and industry. The module culminates in a formal pitch to stakeholders. Integral to this experience is the requirement for students to demonstrate proactive self-development by submitting a Professional Development Training Portfolio, aligning their competencies with contemporary demands in Digital Transformation and AI. This comprehensive experience fosters creativity, strategic agility, ethical leadership, and a commitment to continuous professional growth, preparing students to lead AI-driven innovation in education and beyond. 

Learning and Teaching Methods

Activity Type Hours
Lectures 9
Seminar 12
Practical Classes and Workshops 15
Groupwork 20
Guided Study 60
Problem/Challenge based learning 120
Formative Assessment 4
Summative Assessment 60
Total Hours Selected 300

Learning Outcomes

# Learning Outcome
LO1 Critically evaluate the strategic, ethical, and pedagogical implications of AI-enabled innovation in higher education and demonstrate sophisticated commercial awareness by proactively aligning professional competencies and training with the demands of Digital Transformation and AI-driven economies.
LO2 Synthesise and critically evaluate interdisciplinary knowledge to collaboratively design, develop, and professionally present an ethical AI prototype solution to a strategic challenge, and demonstrate advanced critical reflection on the entire project lifecycle using appropriate meta-cognitive tools.

Module Requisites

N/A

Assessment Criteria

Assessment Category Assessment Type Description Duration Word Count Weight (%) Best of? Pass Mark
Asynchronous Assessment Portfolio Element #1: Group (AI prototype) 60% Equivalence of 1500 words (max.) Element #2: Individual Professional Development Training Portfolio 10% 1000 words (max.) Element #3: Individual (AI guided reflection) 30% 2500 words (max.) 0 N/A 100 No 40

Assessment Matrix

Assessment Type Learning Outcomes
LO1 LO2
Portfolio

Reading List

Week 1 Topic: The \\\"Client Brief\\\" & Business Process Mapping 

Essential Reading 

Hunt, V.D. (1996). Process mapping: how to reengineer your business processes. Chichester: John Wiley & Sons Ltd. 

Supplementary Reading 

Hailu, Tesfaye, and Abdella Kosa Chebo (2024). ‘Mapping Business Process Outsourcing and Innovation towards a Future Research’, Business Process Management Journal, 30(1), pp. 158–182.  

Rowell, J. (2018). ‘Do Organisations Have a Mission for Mapping Processes?’, Business Process Management Journal, 24(1), pp. 2–22.  

Week 2 Topic: The \\\"AI Ecosystem Map\\\" Challenge 

Essential Reading 

Zhavoronok, A., Filyppova, S., Tochylina, Y., Ozarko, K., Neykov, S. and Krylov, D. (2025). ‘The Impact of Artificial Intelligence on the Development of the Digital Business Ecosystem’, Journal of Theoretical and Applied Information Technology, 103(9), pp. 3945 – 3958. 

Supplementary Reading 

Steiber, A. and Alvarez, D. (2024). ‘AI-driven digital business ecosystems: a study of Haier's EMCs’,  European Journal of Innovation Management. Available at https://doi.org/10.1108/EJIM-01-2024-0076. Accessed: 12/09/25. 

Secundo, G., Spilotro, C., Gast, J. and Corvello, V. (2025). ‘The transformative power of artificial intelligence within innovation ecosystems: a review and a conceptual framework’, Review of Managerial Science, 19(9), pp.2697-2728. 

Week 3 Topic: The \\\"AI Innovation\\\" Challenge 

Essential Reading 

Ghaffar, A. and Oyeronke, A. (2025). ‘AI Into Business Automation: Practical Frameworks For Streamlining Operations’. IRE Transactions on Education, 9(1), 1502-1514. 

Ghosh, S. (2025). ‘Developing artificial intelligence (AI) capabilities for data-driven business model innovation: Roles of organizational adaptability and leadership’. Journal of Engineering and Technology Management, 75, pp.1-16. 

Supplementary Reading 

Singh, R., Khan, S., Joshi, A., Raghuveer, K. and Kumar, V. (2025).’ Exploring the role of artificial intelligence on business innovation and entrepreneurship’. Strategy & Leadership. Available at: https://doi.org/10.1108/SL-04-2025-0080. Accessed: 12/09/25. 

Week 4 Topic: The \\\"Justification Matrix\\\" 

Essential Reading 

Debrulle, J., Loïc, P.L.E. and Gardiner, E. (2025). Mastering AI for strategic business success. McGraw Hill. 

Seres, L., Maric, M., Tumbas, P. and Pavlicevic, V. (2019). University stakeholder mapping. In ICERI2019 Proceeding, pp. 9054-9062). 

UNESCO AI Ethics Guidelines. Available at: https://www.unesco.org/en/artificial-intelligence/recommendation-ethics (Accessed 15/09/25). 

Supplementary Reading 

Aithal, P.S. (2025). ‘Strategic Analysis of DeepMind Technologies Limited: An Exploratory Case Study of AI Innovation, Ethics, and Business Evolution’. Poornaprajna International Journal of Teaching & Research Case Studies (PIJTRCS), 2(1), pp.108-147. 

Alhiane, M. and Nafidi, Y. (2025). ‘SWOT Analysis of the Use of Artificial Intelligence Technologies in K-12 Education’. TEM Journal, 14(2), pp. 1355-1366. 

Holzinger, J., Nagl, A., Bozem, K., Lecon, C., Ensinger, A., Roessler, J. and Neufeld, C. (2025). ‘Business Case for a Regional AI-Based Marketplace for Renewable Energies. Sustainability, 17(4), p.1739. 

Weeks 5 - 6 Topic: The \\\"Impact Narrative\\\" Workshop & Peer Review 

Essential Reading 

Elsbach, K.D. (2003). ‘How to pitch a brilliant idea’. Harvard Business Review, 81(9), pp.117-134. 

Supplementary Reading 

Eitan, A. and Fischhendler, I. (2025). ‘Shaping niche innovations in energy transitions: The role of pitching to regulators’. Energy Research & Social Science, 126, p.104170. 

RedkO, O. and MOskalenkO, O. (2021). ‘Psychological Strategies in the Process of Pitching New Business Opportunities.’ Edukacja Ekonomistów i Menedzerów, 59(1), pp.21-30.