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MIS41100

Academic Year 2025/2026

Hot Topics in Analytics (MIS41100)

Subject:
Management Information Systems
College:
Business
School:
Business
Level:
4 (Masters)
Credits:
7.5
Module Coordinator:
Dr Mark Connor
Trimester:
Summer
Mode of Delivery:
On Campus
Internship Module:
No
How will I be graded?
Letter grades

Curricular information is subject to change.

A compressed module that will expose students to cutting edge applications of analytical tools and methods to real-world business problems. Specifically, the module will encompass applications of Agentic AI concepts and frameworks in business along with establishing guardrails and safe implementation of AI Agents.

About this Module

Learning Outcomes:

Learning Outcomes:
On completion of the module students should be able to:
• Compare and contrast AI agents with traditional automation, chatbots, and intelligent assistants
• Understand the principles of AI agent architectures, reasoning patterns, memory, planning, and goal decomposition
• Design and implement AI agents using low-code and pro-code approaches, integrating tools, enterprise data, and external services
• Evaluate AI agents using appropriate frameworks, metrics, and iterative testing methods
• Understand the ethical, safety, privacy, and governance considerations involved in the responsible deployment of AI agents


Indicative Module Content:

• Foundations of Agentic AI
• AI Agent Architectures and Design Patterns
• Agent Memory - Concepts and Types
• Agent Memory - Implementation
• Agent Goal Decomposition - Concepts and Strategies
• Agent Goal Decomposition - Planning and Execution
• Tool Integration and Function Calling - Concepts
• Tool Integration - Building Custom Tools
• Combining It All - Low-Code (Copilot Studio or equivalent)
• Combining It All - Pro-Code (Python, LangChain, LangGraph)
• Evaluation of AI Agents - Frameworks and Metrics
• Evaluation of AI Agents - Implementation and Iteration
• AI Safety, Ethics, and Guardrails

Student Effort Hours:
Student Effort Type Hours
Lectures

30
Specified Learning Activities

42
Autonomous Student Learning

100
Total

172

Approaches to Teaching and Learning:
The module will consist of approximately 30 hours of in-person sessions over a 7-week period, together with other student learning activities:

group work;
lectures;
reflective learning;
enquiry & problem-based learning.

Requirements, Exclusions and Recommendations

Not applicable to this module.


Module Requisites and Incompatibles
Not applicable to this module.
 

Assessment Strategy
Description Timing Component Scale Must Pass Component % of Final Grade In Module Component Repeat Offered
Reflective Assignment: Individual reflective assessment of the group work based assignment Week 10 Graded No
50
 No
Group Work Assignment: Work in a group of 4 students to build a working AI agent for a business scenario using the code and frameworks introduced in the module. Week 9 Graded No
50
 No

Carry forward of passed components
Yes
 

Remediation Type Remediation Timing
In-Module Resit Prior to relevant Programme Exam Board
Please see Student Jargon Buster for more information about remediation types and timing. 

Feedback Strategy/Strategies

• Feedback individually to students, post-assessment
• Group/class feedback, post-assessment

How will my Feedback be Delivered?

Not yet recorded.

Name Role
Aarthi Kumar Lecturer / Co-Lecturer

Timetabling information is displayed only for guidance purposes, relates to the current Academic Year only and is subject to change.
Summer Lecture Offering 51 Week(s) - 37 Fri 10:00 - 11:50
Summer Lecture Offering 51 Week(s) - 38, 39, 40, 41, 42 Fri 10:00 - 11:50
Summer Lecture Offering 51 Week(s) - 43, 44 Fri 10:00 - 11:50
Summer Lecture Offering 51 Week(s) - 37 Fri 13:00 - 14:50
Summer Lecture Offering 51 Week(s) - 38, 39, 40, 41, 42 Fri 13:00 - 14:50
Summer Lecture Offering 51 Week(s) - 43 Fri 13:00 - 14:50
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