Table 7 Recommended responses by AI... | Emerald Publishing

Table 7

Recommended responses by AI distrust dimension

Distrust dimension	Distrust indicators (top quartile/decile)	Pedagogical and governance response
Contextual: Data Protection Distrust	- High scores on this subscale - Frequent student concerns about data handling - High opt-out intention	Prioritizing transparency and safeguards by providing clear guidance on what data may be entered into AI tools, anonymization of sensitive inputs, use of institution-approved platforms, and explicit information on data storage and third-party access
Cognitive: Functional Distrust	- High scores on this subscale - Low usage despite access to AI - Complaints that outputs are inaccurate or not useful	Increasing instructional support and verification practices such as activities to evaluate output quality, instructor demo of strong vs. weak AI outputs, introduction of assessment criteria that reward error-spotting and comparison of AI outputs to validated sources
Emotional: Distrust of AI Replacing Humans	- High scores on this subscale - Resistance to participate in AI-integrated activities - Concerns about reduced human support	Reinforcing human mentoring by explicit positioning of AI as augmentative, increasing instructor interaction, introducing assessments emphasizing judgement and reflection

Distrust dimension	Distrust indicators (top quartile/decile)	Pedagogical and governance response
Contextual: Data Protection Distrust	- High scores on this subscale - Frequent student concerns about data handling - High opt-out intention	Prioritizing transparency and safeguards by providing clear guidance on what data may be entered into AI tools, anonymization of sensitive inputs, use of institution-approved platforms, and explicit information on data storage and third-party access
Cognitive: Functional Distrust	- High scores on this subscale - Low usage despite access to AI - Complaints that outputs are inaccurate or not useful	Increasing instructional support and verification practices such as activities to evaluate output quality, instructor demo of strong vs. weak AI outputs, introduction of assessment criteria that reward error-spotting and comparison of AI outputs to validated sources
Emotional: Distrust of AI Replacing Humans	- High scores on this subscale - Resistance to participate in AI-integrated activities - Concerns about reduced human support	Reinforcing human mentoring by explicit positioning of AI as augmentative, increasing instructor interaction, introducing assessments emphasizing judgement and reflection

Source(s): Authors’ own work