Module Details

Module Code: COMP9095
Title: Advanced Info Design & Develop
Long Title: Advanced Info Design & Development
NFQ Level: Expert
Valid From: Semester 1 - 2022/23 ( September 2022 )
Duration: 1 Semester
Credits: 10
Field of Study: 4811 - Computer Science
Module Delivered in: 2 programme(s)
Module Description: As a key area of technical communications, this module focuses on information design and development concepts and skills to enable information developers to design and deliver effective information for advanced systems, including connected and intelligent systems. The module aims to impart theory and practices of designing and developing information for systems governed by Artificial Intelligence, with a particular focus on Natural Language Processing (NLP), in order to deliver information that is personalised to user contexts.
 
Learning Outcomes
On successful completion of this module the learner will be able to:
# Learning Outcome Description
LO1 Investigate the impact of emerging technologies and machine language processing on information design and delivery
LO2 Determine the impact of automated and Artificial Intelligence systems on information development strategies
LO3 Appraise user context scenarios and multi-dimensional context models
LO4 Design and develop information for multi-dimensional contexts
LO5 Understand conversation design, and develop information using automated systems
LO6 Identify potential ethical and privacy issues related to delivering information via automated machine interfaces
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
Impact of emerging technologies
Explain Industry 4.0 and Information 4.0 concepts and impact of artificial intelligence, IoT, automated systems and machine language processing on information design and development.
Machine language processing
Define natural language processing (NLP), natural language understanding (NLU), and natural language generation (NLG). Develop high-level understanding of how NLP and neural networks work. Explain how NLP impacts information design. Identify challenges of NLP and natural human language.
User context and prediction
Define user context and identify examples. Explain the relationship between information and user context. Understand current research into sentiment analysis and prediction methods. Understand how prediction may be used to drive personalised user information.
Multi-dimensional contexts and information modelling
Define multi-dimensional context. Explain context models. Create strategy for multi-dimensional context models. Build multi-dimensional context models. Map information to user context.
Writing molecular content (micro content)
Understand how molecular content functions in multi-dimensional contexts and user contexts. Design and develop molecular content for user context.
Conversation design
Understand role of conversation design in emerging technologies. Examine rule-based and artificial intelligence systems for conversation design. Provide practical application using automated systems.
Ethics and Privacy
Examine ethical and privacy issues raised by use of automated systems, Artificial Intelligence, IoT, and other emerging technologies. Understand the impact of GDPR and similar regulations on automated systems, including IoT and voice and chatbot technologies.
Module Content & Assessment
Assessment Breakdown%
Coursework100.00%

Assessments

Coursework
Assessment Type Reflective Journal % of Total Mark 10
Timing Every Second Week Learning Outcomes 1,2,3,4,5,6
Assessment Description
Participation in required online discussions based on assigned reading and topics to demonstrate understanding and
exploration of learning outcome subjects.
Assessment Type Project % of Total Mark 30
Timing Week 6 Learning Outcomes 3,4
Assessment Description
Design and develop a multi-dimensional context model.
Assessment Type Project % of Total Mark 60
Timing Sem End Learning Outcomes 3,4,5,6
Assessment Description
Develop an automated or AI-driven bot based on multi-dimensional context using principles and best practices of conversation design.
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 Lecture delivering theory underpinning learning outcomes. Every Week 3.00 3
Tutorial Contact Tutorial to support learning outcomes. Every Week 2.00 2
Independent & Directed Learning (Non-contact) Non Contact Independent study. Every Week 9.00 9
Total Hours 14.00
Total Weekly Learner Workload 14.00
Total Weekly Contact Hours 5.00
Workload: Part Time
Workload Type Contact Type Workload Description Frequency Average Weekly Learner Workload Hours
Lecture Contact Lecture delivering theory underpinning learning outcomes. Every Week 3.00 3
Tutorial Contact Tutorial to support learning outcomes. Every Week 2.00 2
Independent & Directed Learning (Non-contact) Non Contact Independent study. Every Week 9.00 9
Total Hours 14.00
Total Weekly Learner Workload 14.00
Total Weekly Contact Hours 5.00
 
Module Resources
Recommended Book Resources
  • Schwab, K.. (2017), The Fourth Industrial Revolution., Penguin Group, [ISBN: 978-024130075].
  • Nass, C., Brave, S.. (2007), How Voice Activates and Advances the Human-Computer Relationship, The MIT Press, [ISBN: 978-026264065].
Supplementary Book Resources
  • Budinski, Kenneth G.. (2001), Engineers Guide to Technical Writing, ASM International, [ISBN: 9780871706935].
  • Rude, Carolyn D., Eaton, Angela.. (2014), Technical editing., Pearson, [ISBN: 9780205786718].
  • Markel, M., Selber, Stuart A.. (2018), Technical Communication., Macmilan, [ISBN: 9781319058616].
This module does not have any article/paper resources
Other Resources
 
Module Delivered in
Programme Code Programme Semester Delivery
CR_KINDD_9 Master of Science in Technical Communication 2 Mandatory
CR_KIDDE_9 Postgraduate Diploma in Science in Technical Communication 2 Mandatory