Table 4. Brief comparison of research...

Table 4.

Brief comparison of research articles

Lead author (year)	Core focus	Key contribution	Research gap
Singh and Huang (2025)	Practical application of AI in marketing pedagogy	Provides frameworks and strategies to enhance creativity and learning with AI	Empirical measurement of AI’s long-term impact on student creativity and career readiness
Barnayha et al. (2024)	Psychological drivers of online academic dishonesty	Lack of self-control is a key factor in an extended theory of planned behaviour model	Designing and testing interventions to improve self-control in online learning environments
Mumtaz et al. (2024)	Cultural influence on ethical perceptions of AI in education	Ethical views on AI use vary significantly across cultures	Longitudinal studies to see if perceptions change with increased AI exposure; expanding beyond business students
Cotton et al. (2023)	Academic integrity in the era of ChatGPT	Discusses threats and proposes strategies for mitigation	Developing and testing AI-resistant assessment designs and automated integrity tools
Zhang et al. (2023)	Comprehensive survey of Generative AI (AIGC)	A technical review of the state-of-the-art in Generative AI	Addressing real-time capabilities, reducing computational costs and mitigating biases in AIGC
Deng et al. (2023)	Using LLMs to test software (“fuzzing”)	Proposes “FuzzGPT”, using LLMs to find edge-case bugs	Scaling the approach and ensuring the LLM-generated tests are reliable and comprehensive
Lee et al. (2023)	Potential and risks of GPT-4 in medicine	Outlines benefits and serious limitations	Clinical trials to validate GPT-4 utility in specific medical tasks
Timofeyev et al. (2023)	Impact of transparency on healthcare efficiency	Lack of transparency hinders the efficiency of a healthcare system	Developing and implementing specific transparency frameworks and measuring their impact on outcomes
Guruswamy et al. (2022)	Applying Industry 5.0 concepts to food security	Proposes a human-centric, resilient food system using advanced tech	Pilot projects to implement and test the proposed tech-concept framework in real-world supply chains
Hendy (2021)	Cultural differences in academic dishonesty	Cultural norms influence dishonest behaviours (social learning perspective)	Investigating how digital culture and globalized online learning interact with national culture
Parks-Leduc et al. (2021)	Effectiveness of ethics training	Distributed ethics training can improve ethical decision-making	Testing the efficacy of AI-specific ethics training modules for students and professionals
Peled et al. (2019)	Predictors of academic dishonesty in online vs traditional courses	Identifies key predictors across course formats	Exploring how the rise of generative AI has changed these predictors and their relative weight
Lehnert et al. (2015)	Review of ethical decision-making literature	Identifies boundary conditions and gaps in ethical decision-making models	Updating the framework to include decisions involving AI and automation
Williams (2014)	Foundational science of biomaterials	A textbook on principles of materials for medical implants	Research into next-generation smart biomaterials with integrated sensing and adaptive properties

Lead author (year)	Core focus	Key contribution	Research gap
Singh and Huang (2025)	Practical application of AI in marketing pedagogy	Provides frameworks and strategies to enhance creativity and learning with AI	Empirical measurement of AI’s long-term impact on student creativity and career readiness
Barnayha et al. (2024)	Psychological drivers of online academic dishonesty	Lack of self-control is a key factor in an extended theory of planned behaviour model	Designing and testing interventions to improve self-control in online learning environments
Mumtaz et al. (2024)	Cultural influence on ethical perceptions of AI in education	Ethical views on AI use vary significantly across cultures	Longitudinal studies to see if perceptions change with increased AI exposure; expanding beyond business students
Cotton et al. (2023)	Academic integrity in the era of ChatGPT	Discusses threats and proposes strategies for mitigation	Developing and testing AI-resistant assessment designs and automated integrity tools
Zhang et al. (2023)	Comprehensive survey of Generative AI (AIGC)	A technical review of the state-of-the-art in Generative AI	Addressing real-time capabilities, reducing computational costs and mitigating biases in AIGC
Deng et al. (2023)	Using LLMs to test software (“fuzzing”)	Proposes “FuzzGPT”, using LLMs to find edge-case bugs	Scaling the approach and ensuring the LLM-generated tests are reliable and comprehensive
Lee et al. (2023)	Potential and risks of GPT-4 in medicine	Outlines benefits and serious limitations	Clinical trials to validate GPT-4 utility in specific medical tasks
Timofeyev et al. (2023)	Impact of transparency on healthcare efficiency	Lack of transparency hinders the efficiency of a healthcare system	Developing and implementing specific transparency frameworks and measuring their impact on outcomes
Guruswamy et al. (2022)	Applying Industry 5.0 concepts to food security	Proposes a human-centric, resilient food system using advanced tech	Pilot projects to implement and test the proposed tech-concept framework in real-world supply chains
Hendy (2021)	Cultural differences in academic dishonesty	Cultural norms influence dishonest behaviours (social learning perspective)	Investigating how digital culture and globalized online learning interact with national culture
Parks-Leduc et al. (2021)	Effectiveness of ethics training	Distributed ethics training can improve ethical decision-making	Testing the efficacy of AI-specific ethics training modules for students and professionals
Peled et al. (2019)	Predictors of academic dishonesty in online vs traditional courses	Identifies key predictors across course formats	Exploring how the rise of generative AI has changed these predictors and their relative weight
Lehnert et al. (2015)	Review of ethical decision-making literature	Identifies boundary conditions and gaps in ethical decision-making models	Updating the framework to include decisions involving AI and automation
Williams (2014)	Foundational science of biomaterials	A textbook on principles of materials for medical implants	Research into next-generation smart biomaterials with integrated sensing and adaptive properties

Source(s): Mumtaz et al. (2024), Singh and Huang (2025), Barnayha et al. (2024), Lehnert et al. (2015), Zhang et al. (2023), Timofeyev et al. (2023), Deng et al. (2023), Lee et al. (2023), Cotton et al. (2023), Hendy et al. (2021), Peled et al. (2019), Williams (2014), Guruswamy et al. (2022), Parks-Leduc et al. (2021)

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