Module Code: H9RCM
Long Title Risk and Change Management
Title Risk and Change Management
Module Level: LEVEL 9
EQF Level: 7
EHEA Level: Second Cycle
Credits: 5
Module Coordinator: Rejwanul Haque
Module Author: Shauni Hegarty
Departments: School of Computing
Specifications of the qualifications and experience required of staff

PhD/Master’s degree in a computing or cognate discipline. May have industry experience also. 
Learning Outcomes
On successful completion of this module the learner will be able to:
# Learning Outcome Description
LO1 Demonstrate expert knowledge of the principles and models of change management, and how these can support the adoption of AI within organisations.
LO2 Comprehend, compare, and contrast governance, risk, and compliance (GRC) for AI and its impacts on traditional information governance.
LO3 Select, assess, and apply best practices to structure AI teams, manage roles and responsibilities, and govern AI projects to drive responsible and ethical outcomes.
LO4 Evaluate and communicate changes related to the organisation and AI.
Dependencies
Module Recommendations

This is prior learning (or a practical skill) that is required before enrolment on this module. While the prior learning is expressed as named NCI module(s) it also allows for learning (in another module or modules) which is equivalent to the learning specified in the named module(s).
No recommendations listed
Co-requisite Modules
No Co-requisite modules listed
Entry requirements

Applicants are required to hold a minimum of a Level 8 honours qualification (2.2 or higher) or equivalent on the National Qualifications Framework in either STEM (e.g., Information Management Systems, Information Technologies, Computer Science, Computer Engineer) or Business (e.g., Business Information Systems, Business Administration, Economics) discipline and a minimum of three years of relevant work experience in industry, ideally but not necessarily, in management. Previous numerical and computer proficiencies should be part of their work experience or formal training. Graduates from disciplines which do not have technical or mathematical problem-solving skills embedded in their programme will need to be able to demonstrate technical or mathematical problem-solving skills in addition to their level 8 programme qualifications (Certifications, Additional Qualifications, Certified Experience and Assessment Tests). All applicants for the programme must provide evidence that they have prior Mathematics and Computing module experience (e.g., via academic transcripts or recognised certification) as demonstrated in one mathematics/statistics module and one computing module or statement of purpose must specify numerical and computing work experience. 

NCI also operates a prior experiential learning policy where graduates with lower, or no formal qualifications, currently working in a relevant field, may be considered for the programme. 

Applicants must also be able to have their own laptop with the minimum required specification that will be communicated to each applicant through both the admissions and marketing departments. 
 

Module Content & Assessment

Indicative Content
No indicative content
Assessment Breakdown%
Coursework100.00%

Assessments

Full Time

Coursework
Assessment Type: Formative Assessment % of total: Non-Marked
Assessment Date: n/a Outcome addressed: 1,2,3,4
Non-Marked: Yes
Assessment Description:
Formative assessment will be provided on the in-class individual or group activities. Feedback will be provided in written or oral format, or online through Moodle. In addition, in class discussions will be undertaken as part of the practical approach to learning.
Assessment Type: Continuous Assessment % of total: 100
Assessment Date: n/a Outcome addressed: 1,2,3,4
Non-Marked: No
Assessment Description:
LO1 – LO4 are achieved in two stages. First, course content is reviewed, discussed, and worked out in the form of a framework to solve a real-life business situation with AI technology. Secondly, that formulation work is applied to the problem and a solution is presented, consolidating learning. Learners will present technical components of the solution in a simulated Technical advisory board and change advisory board following the change management process.
No End of Module Assessment
No Workplace Assessment
Reassessment Requirement
Coursework Only
This module is reassessed solely on the basis of re-submitted coursework. There is no repeat written examination.

NCIRL reserves the right to alter the nature and timings of assessment

 

Module Workload

Module Target Workload Hours 0 Hours
 

Module Resources

Recommended Book Resources
  • Marlon Dumas,Marcello La Rosa,Jan Mendling,Hajo A. Reijers. (2019), Fundamentals of Business Process Management, Springer, p.527, [ISBN: 978-3662585856].
  • Tom Taulli. (2020), The Robotic Process Automation Handbook, Apress, p.344, [ISBN: 978-1484257289].
  • Stuart Russell,Peter Norvig. (2019), Artificial Intelligence, Pearson Higher Education, p.1136, [ISBN: 978-0134610993].
  • Nandan Mullakara,Arun Kumar Asokan. Robotic Process Automation Projects, [ISBN: 978-1839217357].
Supplementary Book Resources
  • Elijah Falode. The Future of Intelligent Automation, [ISBN: 979-8642979969].
  • Elijah Falode. The Future of Intelligent Automation, [ISBN: 979-8642979969].
  • Walter Surdak. The Care and Feeding of Bots, [ISBN: 979-8610003634].
  • Olivier Boissier,Rafael H. Bordini,Jomi Hubner,Alessandro Ricci. (2020), Multi-Agent Oriented Programming, MIT Press, p.264, [ISBN: 978-0262044578].
This module does not have any article/paper resources
Other Resources
Discussion Note: