Bridging the Divide: An Empirical Investigation of Artificial Intelligence and Generative Artificial Intelligence Integration Across Genders, Disciplines and Academic Roles

Knowledge and actual use of AI-based tools in female and male

		Male (%)	Female (%)	χ2(2)	P-value
Familiar with AI and GenAI-based tools	Minimally	95 ((40	241 (54)	25.11	<0.0001
	Moderately	78 ((33	148 ((33
	Highly	64 (27)	58 ((13
	Total	237 (100)	447 (100)
Actual use of AI and GenAI for teaching/research purposes	Used	105 (46)	191 (44)	5.41	0.0669
	Have not used but intend to use	81 (35)	186 (43)
	Have not used and do not intend to use	45 (20)	60 (14)
	Total	231 (100)	437 (100)

Knowledge and actual use of AI and GenAI-based tools in lecturers and students

		Lecturers (%)	Students (%)	χ2(2)	P-value
AI and GenAI studies should be integrated into academic staff	Minimally agree	6 (3)	33 (7)	25.11	<0.0001
	Moderately agree	39 (20)	153 (33)
	Highly agree	152 (77)	279 (60)
	Total	197 (100)	465 (100)
Actual use of AI and GenAI for teaching/research purposes	Used	109 (56)	178 (39)	18.29	<0.0001
	Have not used but intend to use	66 (34)	191 (42)
	Have not used and do not intend to use	20 (10)	85 (19)
	Total	195 (100)	454 (100)

Sociodemographic characteristics of the participants

Variable	Sub-variable	Frequency (%)
Gender	Male	238 (35)
	Female	448 (65)
	Total	686 (100)
Academic status	Student	471 (69)
	Lecturer	197 (29)
	Other	16 (2)
	Total	684 (100)
Faculty	Humanities	89 (19)
	Social sciences	167 (36)
	Social welfare and health sciences	94 (21)
	Engineering and exact sciences	25 (5)
	Natural and environmental sciences	29 (6)
	Law	21 (5)
	Other or multidisciplinary	34 (7)
	Total	459 (100)

Differences in the perception of the risks of AI in academies between lecturers and students

		Lecturers	Students	χ2(1)	P-value
Bias in algorithms or data	Frequency (%)	119 (62)	244 (53)	4.50	0.0339
Compromising work efficiency	Frequency (%)	12 (6)	55 (12)	4.75	0.0293
Transparency issues in AI and GenAI processes	Frequency (%)	123 (64)	207 (45)	19.02	<0.0001
A threat to existing academic	Frequency (%)	19 (10)	92 (20)	8.68	0.0032
positions	Total	192* (100)	461* (100)

Differences in the perception of the benefits of AI and GenAI in teaching and research in academia between men and women and between lecturers and students

Teaching		Male	Female	χ2(1)	P-value
Decision support tool	Frequency (%)	79 (35)	98 (2)	9.99	0.0016
	Total	226 (100)	424 (100)
Research
Analysing written language using natural language models	Frequency (%)	70 (31)	103 (24)	4.09	0.43
	Total	227 (100)	430 (100)
		Lecturers	Students	χ2(1)	P-value
Teaching
Improving student engagement and persistence	Frequency (%)	58 (32)	108 (24)	3.92	0.0478
Systematic tracking of learning process outcomes	Frequency (%)	38 (21)	216 (48)	39.37	<0.0001
Using AI and GenAI as a consultant for lecturers	Frequency (%)	86 (48)	103 (23)	38.5	<0.0001
Enhancing accessibility and inclusion	Frequency (%)	36 (20)	144 (32)	9.48	0.0021
	Total	180 (100)	449 (100)
Research
Assistance in literature writing	Frequency (%)	108 (58)	204 (45)	7.66	0.0056
Support in data analysis and interpretation	Frequency (%)	84 (45)	258 (57)	8.35	0.0039
Prediction models based on existing data to guide future research	Frequency (%)	48 (26)	245 (54)	40.22	<0.0001
Connecting researchers from similar fields and promoting collaborations	Frequency (%)	33 (18)	149 (33)	14.48	0.0001
	Total	186 (100)	453 (100)