When artificial intelligence substitutes humans in higher education: the cost of loneliness, student success, and retention

Figures & data

Figure 1. Theoretical model.

Table 1. Sample demographics.

		f	%
Gender	Female	261	67.4%
	Male	116	30.0%
	Non-binary	6	1.6%
	Other/Prefer not to say	4	1.0%
Level of study	Undergraduate	128	33.1%
	Postgraduate	223	57.6%
	Doctoral	36	9.3%
Ethnicity	Caucasian	222	57.4%
	Asian	103	26.6%
	Black/African American	31	8.0%
	Native/indigenous	2	0.5%
	Other/Prefer not to say	29	7.5%
Country of residence	United Kingdom	135	34.9%
	Australia	66	17.1%
	United States of America	47	12.1%
	Germany	21	5.4%
	Other	118	30.5%
Socioeconomic status	Low	35	9%
	Lower-middle	107	27.6%
	Middle	168	43.4%
	Upper-middle	71	18.3%
	High	6	1.6%

Table 2. Competing models for the structural equation model.

Model	X^2/df	p	CFI	TLI	RMSEA	SRMR
Model 1. Mediated effects with a single social support measure.	1.372	.09	.98	.97	.03	.04
Model 2. Mediated effects with AI and human social support measure as independent.	1.002	.46	.99	.99	.002	.04
Model 3. Direct effects of AI on GPA and Retention	1.708	.15	.99	.97	.04	.03
Model 4. When AI usage is removed from Model 1.	4.77	.00	.90	.79	.10	.05
Model 5. When AI use and social support are removed from Model 1.	2.698	.00	.96	.92	.07	.38
Model 6. When AI use and social support are removed from Model 2.	1.183	.31	.97	.99	.02	.02

Figure 2. AI and human mediated effects on loneliness, GPA, and retention (Model 1).

Figure 3. AI and human mediated effects on loneliness, GPA, and retention when AI-based social support is independent of human support.

Figure 4. AI and human insignificant effects on GPA and retention.