ULMS Electronic Module Catalogue
The information contained in this module specification was correct at the time of publication but may be subject to change, either during the session because of unforeseen circumstances, or following review of the module at the end of the session. Queries about the module should be directed to the member of staff with responsibility for the module.
Title AI Applications and Innovations
Code EBUS637
Coordinator Dr OSM Khaled Elsayed
Operations and Supply Chain Management
Omar.Elsayed@liverpool.ac.uk
Year CATS Level Semester CATS Value
Session 2024-25 Level 7 FHEQ First Semester 15

Pre-requisites before taking this module (other modules and/or general educational/academic requirements):
 

Modules for which this module is a pre-requisite:
 

Programme(s) (including Year of Study) to which this module is available on a required basis:
 

Programme(s) (including Year of Study) to which this module is available on an optional basis:
 

Teaching Schedule
  Lectures Seminars Tutorials Lab Practicals Fieldwork Placement Other TOTAL
Study Hours 20

 5

         25
Timetable (if known)              
Private Study 125
TOTAL HOURS 150

Assessment
EXAM Duration Timing
(Semester)		 % of
final
mark		 Resit/resubmission
opportunity		 Penalty for late
submission		 Notes
             
CONTINUOUS Duration Timing
(Semester)		 % of
final
mark		 Resit/resubmission
opportunity		 Penalty for late
submission		 Notes
Group Presentation Reassessment Opportunity: Yes Penalty for Late Submission: Standard UoL Penalty Applies Anonymous Assessment: No Final Assessment: No  15    60       
Individual Project Reassessment Opportunity: Yes Penalty for Late Submission: Standard UoL Penalty Applies Anonymous Assessment: Yes Final Assessment: Yes  0    40       

Aims

This module aims to provide students with a deep understanding of the principles and practices behind AI technologies (including generative AI) and their development.

The objectives of the module are to:

Enable students to effectively align AI initiatives with business strategies, ensuring that AI applications drive value and support organisational goals;

Equip students with practical skills in applying various AI tools and techniques in real-world business scenarios across various sectors and industries;

Encourage innovative thinking in business model design, leveraging AI to create competitive advantages and adapt to market changes and challenges.

Learning Outcomes

(LO1) Students will be able to analyse the principles and practices of AI technologies.

(LO2) Students will be able to examine the fundamentals and the models behind generative AI techniques.

(LO3) Students will be able to scrutinise the value of generative AI in supporting organisational goals in order to achieve competitive advantage.

(LO4) Students will be able to critically analyse and evaluate the application of various AI tools and techniques in real-world business scenarios across various sectors and industries.

(LO5) Students will be able to demonstrate innovative thinking in business model design by leveraging AI.

(S1) Innovation
Students will develop innovative thinking through case study work and assignments through developing innovative artificial intelligence models.

(S2) Problem solving
Students will develop their problem-solving skills through practical exercises and through undertaking assignments.

(S3) Commercial awareness
Students will develop knowledge of commercial contexts associated with artificial intelligence application in several industries.

(S4) Critical thinking skills
Students will develop their critical thinking skills through undertaking assignments.

(S5) IT literacy
IT skills will be developed during practical lab sessions.

(S6) International awareness
Students will develop their international awareness through case study work associated with innovative business models and AI technologies in an international context.

(S7) Lifelong learning skills
Students will develop their lifelong-learning skills through preparation for their assessments and self-directed study of cases in preparation for class discussions.

(S8) Teamwork and Leadership
Students will develop teamwork, collaboration and leadership skills by working in groups for class activities and the presentation.

(S9) Presentation and Communication skills
Students will develop presentation and communication skills by engaging with case studies and working in groups for their presentations.

Teaching and Learning Strategies

Lectures x 20 hours (10 lectures of 2 hours each)
Students will attend a weekly 2 hour lecture during which the key concepts will be introduced.

Seminars x 5 hours (5 seminars of 1 hour each)
During these sessions, students will be exposed to several AI tools and models.

Self-Directed Learning x 125 hours
Self-directed learning hours are aimed at supporting the directed student learning. The module leader will provide guidance in the form of suggested readings or topics to be completed outside of the classroom with the expectation that students will be well prepared to contribute to the seminar activities and to understand the content of lectures.

Syllabus
 

The module covers the following topics:

Fundamentals of AI;

Generative models and techniques;

Machine learning and deep learning;

AI applications in Natural Language Processing (NLP), image generation and data analytics;

Case studies and real-world applications of AI in various industries;

Future trends and emerging technologies.

Recommended Texts
Reading lists are managed at readinglists.liverpool.ac.uk. Click here to access the reading lists for this module.