Artificial intelligence education for radiographers, an evaluation of a UK postgraduate educational intervention using participatory action research: a pilot study

Insights into Imaging

Table 3 Codes, code frequency and convergence of codes to construct the themes

Codes	Frequency (n)	Themes
Student participants
Buffer of experience	4	Theme 1: Participants’ professional and educational background influenced their experiences
Met learning needs as an introduction	2
Faculty participants
Assume no knowledge to pitch content and learning activities	1
Background influenced preparation/experience	1
Student participants
Mode of delivery	25	Theme 2: A meaningful learning experience
Views on assignments	14
High quality content	15
Knowledge gained	15
Online interactions	9
Module length	7
Different didactic/pedagogical approaches	7
Module organisation	11
Student participants
Views on assignments	14	Theme 3: Barriers to learning and threats to module status
Module length	7
Timing—presentation	3
Cost	2
Certification issues	2
Technology mediated learning as a barrier	1
Preparation must be linked to lesson	1
Socialisation missed	1
Student participants
Focus on how AI works	20	Theme 4: The ideal introductory AI module
Balance between cohort, intention and content is important—iterative process	3
Selection of learning experiences need to be purposeful and not repetitive	4
Access to materials after the module	2
Important to include in curricula	3
Faculty participants
Focus on how AI works	18
Balance between cohort, intention and content is important—iterative process	3
Important to include in curricula	2
Selection of learning experiences need to be purposeful and not repetitive	3
Consider IPE approach for imaging professionals	5