Module Details

The information contained in this module specification was correct at the time of publication but may be subject to change, either during the session because of unforeseen circumstances, or following review of the module at the end of the session. Queries about the module should be directed to the member of staff with responsibility for the module.
Title Artificial Intelligence and Communication B
Code COMM718
Coordinator Dr E Musi
Communication and Media
Elena.Musi@liverpool.ac.uk
Year CATS Level Semester CATS Value
Session 2021-22 Level 7 FHEQ Second Semester 15

Aims

To provide insight into the way Artificial Intelligence is influencing our communication practices
To introduce students with analytic means to navigate the complexity of communication in the AI era
To explore the changes brought about by AI through the introduction of digital media as well as human-computer interactions
To encourage students to reflect on the impact that AI is having on their communication processes
To provide students with the means to assess risks and opportunities introduced by the use of AI in the communication environment


Learning Outcomes

(LO1) Students will demonstrate knowledge about core notions to investigate the relations between AI and communication.

(LO2) Students will analyse data privacy issues in view of algorithm transparency norms.

(LO3) Students will critically analyse cases of (mis)information diffusion.

(LO4) Students will evaluate risks and opportunities that the use of AI tools create in daily communication practices.

(S1) Problem solving and critical thinking

(S2) Ethical awareness

(S3) Digital Communication skills

(S4) Application of discourse analysis tools


Syllabus

 

Topics may include:

Introduction
Communication in the Network Society
Human-computer interaction
Communication/data privacy
Interaction with (Conversational) bots
Digital lines of information diffusion
Internet Health report
Online audiences, identities and anonymity
(Mis)information spread, fake news and factchecking
Biased communications
Algorithm transparencies
Module Overview and Revision

Independent Study Week will be in week 7.


Teaching and Learning Strategies

Teaching Method 1: Workshop
Description: The 3-hour continuous workshops will combine lectures with group activities and seminar discussions. The lecture phase will introduce the key concepts, approaches and tools. Such notions will be applied through the analysis of case studies in the group work component. The seminar components will be devoted to the critical discussion of different perspectives and issues arisen during group activities. Seminars will allow students to explore and strengthen their understanding of different approaches through practice.
Attendance Recorded: yes
Self-Directed Learning Description: Independent study time will be devoted to read relevant literature listed in the provided reading lists and to familiarise with the analytical tools introduced in class.


Teaching Schedule

  Lectures Seminars Tutorials Lab Practicals Fieldwork Placement Other TOTAL
Study Hours           36

36
Timetable (if known)              
Private Study 264
TOTAL HOURS 300

Assessment

EXAM Duration Timing
(Semester)
% of
final
mark
Resit/resubmission
opportunity
Penalty for late
submission
Notes
             
CONTINUOUS Duration Timing
(Semester)
% of
final
mark
Resit/resubmission
opportunity
Penalty for late
submission
Notes
Test focusing on the main analytic tools.  120 minutes    100       

Recommended Texts

Reading lists are managed at readinglists.liverpool.ac.uk. Click here to access the reading lists for this module.