Academic Handbook Course Descriptors and Programme Specifications
LCSCI4273A Digital Fluency in the Artificial Intelligence-Enabled Enterprise Course Descriptor
Course code | LCSCI4273A | Discipline | Computer Science |
UK Credit | 15 | US Credit | N/A |
FHEQ level | 4 | Date approved | October 2023 |
Compulsory/ Optional |
Compulsory | ||
Pre-requisites | None | ||
Co-requisites | None |
Course Overview
This course is delivered as a two-week, intensive, face-to-face bootcamp. Organisational leaders preparing for the future of digital advancement recognise the importance of accurately assessing the value of information resources, improving processes at all levels of the organisation, and preparing their workforce to make the most efficient and effective use of information systems deployed. Leaders faced with the challenge of preparing themselves and others for the next generation of human-computer interaction recognise that technology is a double-edged sword that presents opportunities and threats simultaneously. To meet this challenge, leaders must improve their digital fluency – the syntax knowledge, sociolinguistic sensibility, accessibility, sustainability issues – and strategic expertise that a person gains and demonstrates in their use of information resources.
This course considers cognitive frameworks, including critical thinking, design thinking, and systems thinking. It introduces learners to the integration of artificial intelligence into information systems, fostering an understanding of its applications. Learners gain insights into the multifaceted aspects of AI, discerning its value, potential, and challenges. The course focuses on design and deployment aspects, enabling participants to focus on the practical implementation of AI rather than development and coding.
Learning Outcomes
On successful completion of the course, students will be able to:
Knowledge and Understanding
K1a(i) | Explain and apply the attributes of digital fluency in a real-world context in order to identify opportunities for growth in selves and teams. |
K1a(ii) | Identify and evaluate the accessibility and sustainability of artificial intelligence solutions. |
K2a | Identify the pitfalls and challenges associated with the deployment of artificial intelligence in the enterprise. |
Subject Specific Skills
S1a | Apply an appropriate artificial intelligence systems evaluation framework to accurately assess the value of artificial intelligence deployment at each level of the organisation (strategic, tactical, and operational). |
S2a | Synthesise the attributes of critical thinking, design thinking, and systems thinking and apply them to artificial intelligence deployment in a real-world situation. |
Transferable and Employability Skills
T1a(i) | Develop leadership and management skills. |
T1a(ii) | Display a developing technical proficiency in written English and an ability to communicate clearly and accurately in structured and coherent pieces of writing. |
T2a | Demonstrate time-management and organisational skills within the context of self-directed learning. |
T3a | Demonstrate the ability to obtain and use information from a variety of sources as part of self-directed learning. |
Teaching and Learning
This is a face-to-face bootcamp, of two weeks duration, taught once every year.
This course can be offered as a standalone short course.
Teaching and learning strategies for this course will include:
- Lectures
- Informal discussion groups
- Practical sessions
- Assessment
Course information and supplementary materials will be available on the University’s Virtual Learning Environment (VLE).
Learners are required to attend and participate in all the formal and timetabled sessions for this course. Learners are also expected to manage their self-directed learning and independent study in support of the course.
The course learning and teaching hours will be structured as follows:
- Off-the-job learning and teaching (12 days x 7 hours) = 84 hours
- On-the-job learning (10 days x 7 hours) = 70 hours
Apprentices will complete workplace activities before and after the bootcamp, as part of their on-the-job learning. Preliminary activities include reading and workplace research and post-bootcamp activities will include completion of the assignments (see below).
Assessment
Formative
Learners will be formatively assessed during the course by means of set assignments. These will not count towards the final degree but will provide learners with developmental feedback.
Summative
Assessment will be in two forms:
AE | Assessment Type | Weighting | Online submission | Duration | Length |
1 | Written Assignment | 70% | Yes | – | 2,500 words, excluding data tables |
2 | Presentation | 30% | Yes | 30 mins | – |
Feedback
Learners will receive formal feedback in a variety of ways: written (via email correspondence); oral and indirectly through discussion during group tutorials. Learners will also attend a formal meeting with their Academic Mentor and Employer. These tri-partite reviews will monitor and evaluate the learner’s progress.
Feedback is provided on summatively assessed assignments and through generic internal examiners’ reports, both of which are posted on the VLE.
Indicative Reading
Note: Comprehensive and current reading lists for courses are produced annually in the Course Syllabus or other documentation provided to learners; the indicative reading list provided below is used as part of the approval/modification process only.
Books
- Briggs, C. and Makice, K., (2012), Digital Fluency: Building Success in the Digital Age, Digital Fluency
- Callan, R., (2003), Artificial Intelligence, Basingstoke: Palgrave Macmillan
- Van Emden, J. and Becker, L., (2016), Presentation Skills for Students, Basingstoke: Palgrave Macmillan
Journals
Learners are encouraged to read material from relevant journals on Digital Fluency and/or Artificial Intelligence as directed by their course trainer.
Electronic Resources
Learners are encouraged to consult websites on Digital Fluency and/or Artificial Intelligence.
Indicative Topics
- Artificial Intelligence within Information Systems
- Recommendation Engines
- Voice-Activated Transaction Processing
Version History
Title: LCSCI4273A Digital Fluency in the Artificial Intelligence-Enabled Enterprise Course Descriptor
Approved by: Academic Board Location: Academic Handbook/BSc (Hons) Digital & Technology Solutions |
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Version number | Date approved | Date published | Owner | Proposed next review date | Modification (As per AQF4) & category number |
4.0 | October 2023 | October 2023 | Dr Alexandros Koliousis | October 2028 | Category 1: Corrections/clarifications to documents which do not change approved content.
Category 3: Changes to Learning Outcomes |
3.0 | October 2022 | January 2023 | Scott Wildman | June 2025 | Category 1: Corrections/clarifications to documents which do not change approved content.
Category 3: Changes to Learning Outcomes |
2.1 | May 2022 | May 2022 | Scott Wildman | June 2025 | Category 1: Corrections/clarifications to documents which do not change approved content. |
2.0 | January 2022 | April 2022 | Scott Wildman | June 2025 | Category 3: Changes to Learning Outcomes |
1.0 | June 2020 | June 2020 | Scott Wildman | June 2025 |