MGMT8082 - AI for Professional Practice

Module Details

Module Code: MGMT8082
Title: AI for Professional Practice
Long Title: AI for Professional Practice
NFQ Level: Advanced
Valid From: Semester 1 - 2024/25 ( September 2024 )
Duration: 1 Semester
Credits: 5
Field of Study: 3450 - Business & Management
Module Delivered in: no programmes
Module Description: This module will provide students with skills for using generative AI as a virtual assistant in the workplace. Learners will build their knowledge of the terminology of AI and provide them with the skills to develop AI policies and best practice procedures. The practical elements will ensure that students can identify suitable AI tools for specific tasks as well as build practical skills with AI tools.
 
Learning Outcomes
On successful completion of this module the learner will be able to:
# Learning Outcome Description
LO1 Discuss and examine generative AI tools
LO2 Critique how generative AI tools can be used to assist in day-to-day processes and as personal assistants
LO3 Evaluate the ethical issues and concerns for organisations around generative AI tools
LO4 Demonstrate best practices when using proprietary data and generative AI tools
LO5 Construct and produce a selection of professional documents using a variety of generative AI tools
Dependencies
Module Recommendations

This is prior learning (or a practical skill) that is strongly recommended before enrolment in this module. You may enrol in this module if you have not acquired the recommended learning but you will have considerable difficulty in passing (i.e. achieving the learning outcomes of) the module. While the prior learning is expressed as named MTU module(s) it also allows for learning (in another module or modules) which is equivalent to the learning specified in the named module(s).
Incompatible Modules
These are modules which have learning outcomes that are too similar to the learning outcomes of this module. You may not earn additional credit for the same learning and therefore you may not enrol in this module if you have successfully completed any modules in the incompatible list.
No incompatible modules listed
Co-requisite Modules
No Co-requisite modules listed
Requirements

This is prior learning (or a practical skill) that is mandatory before enrolment in this module is allowed. You may not enrol on this module if you have not acquired the learning specified in this section.
No requirements listed
 
Indicative Content
Introduction to Generative AI Tools
History of generative AI, AI terminology, definition of AI and generative AI, types of generative AI tools such as image generation tools, text generation tools, music generation tools, code generation tools. Application of generative AI such as creative content creation, product development and personalisation. Examples of generative AI in use.
Generative AI in use in Operational Processes
Generative AI tools assisting in operational processes such as content creation, data analysis, product development, customer service. Examples of generative AI tools used in operational processes.
Generative AI as Personal Assistants
Generative AI tools as personal assistants for scheduling, translation, research and other activities. Examples of generative AI tools in use as personal assistants.
Ethical Issues
Ethical issues and concerns such as misinformation and deepfakes, privacy and data security, content ownership and copyright, amplification and bias, job displacement, lack of transparency and explainability. Development of clear policies and guidelines, training and awareness, openness and transparency and accountability and oversight.
Best Practice when using Proprietary Data
Define the specific purpose for using proprietary data, deidentifying data, monitoring data usage, establish data ownership and usage policies, establish an audit trail, review and update policies. Consider data quality, data bias and data governance when using proprietary data.
Generate Professional Documents and Papers
Using a variety of generative AI tools generate various professional documents and papers such as presentations, press releases, business plan, reports, business policies, marketing plan, digital content creation such as social media and website content.
Module Content & Assessment
Assessment Breakdown%
Coursework100.00%

Assessments

Coursework
Assessment Type Written Report % of Total Mark 30
Timing Week 6 Learning Outcomes 1,2,3,4
Assessment Description
Develop a generative AI policy for an organisation of your choice.
Assessment Type Essay % of Total Mark 40
Timing Week 12 Learning Outcomes 1,2,3,5
Assessment Description
Carry out a comparative study of three AI tools. Use a generative AI tool to compare your three chosen generative AI tools. Compare and contrast your essay with the output from the generative AI tool.
Assessment Type Practical/Skills Evaluation % of Total Mark 30
Timing Every Second Week Learning Outcomes 5
Assessment Description
Use generative AI tools to create a selection of professional documents.
No End of Module Formal Examination
Reassessment Requirement
Coursework Only
This module is reassessed solely on the basis of re-submitted coursework. There is no repeat written examination.

The University reserves the right to alter the nature and timings of assessment

 

Module Workload

Workload: Full Time
Workload Type Contact Type Workload Description Frequency Average Weekly Learner Workload Hours
Lecture Contact Class based instruction Every Week 2.00 2
Lab Contact Computer lab using a variety of generative AI tools Every Week 1.00 1
Independent & Directed Learning (Non-contact) Non Contact Self directed study Every Week 4.00 4
Total Hours 7.00
Total Weekly Learner Workload 7.00
Total Weekly Contact Hours 3.00
Workload: Part Time
Workload Type Contact Type Workload Description Frequency Average Weekly Learner Workload Hours
Lecture Contact Class based instruction Every Week 1.00 1
Lab Contact Computer lab using a variety of generative AI tools Every Week 1.00 1
Independent & Directed Learning (Non-contact) Non Contact Self directed study Every Week 5.00 5
Total Hours 7.00
Total Weekly Learner Workload 7.00
Total Weekly Contact Hours 2.00
 
Module Resources
Recommended Book Resources
  • Matt White. (2024), Generative AI for Business, Wiley, [ISBN: 978-1394197118].
  • Kavita Ganesan. (2022), The Business Case for AI, Opinosis Analytics Publishing, p.316, [ISBN: 978-1544528724].
  • Bernard Marr. (2024), Generative AI in Practice, Wiley, [ISBN: 978-1394245567].
Supplementary Book Resources
  • Bob Pellerin. (2023), AI Business Strategies: Leveraging Artificial Intelligence as a Competitive Advantage, [ISBN: 979-8393464110].
  • Lyle Yorks,Amy Lui Abel,Denise Rotatori. (2022), Strategic Human Resource Development in Practice, Springer, [ISBN: 978-3030957742].
  • Ben Eubanks. Artificial Intelligence for HR, Kogan Page, [ISBN: 978-0749483814].
Recommended Article/Paper Resources
Other Resources