Factors Influencing Preschool Teachers’ Continuous Intention to Use AI Generated Content in Education

DEMOGRAPHIC FACTORS	CATEGORIES	FREQUENCY	PERCENTAGE (%)
Gender	Female	259	59.8
Gender	Male	174	40.1
Age	20–29	50	11.5
	30–39	344	79.4
	40–49	24	5.5
	Above 50	15	3.4
Educational level	Bachelor	253	58.4
	Master	146	33.7
	PhD	34	7.8
Type of institution	Public	276	63.7
Type of institution	Private	157	36.3
Teaching experience	Less than 5 years	102	23.6
	5–10 years	168	38.8
	11–15 years	95	21.9
	More than 15 years	68	15.7
Region	East	216	50
	Central	95	22
	West	87	20
	Northeast	35	8

Table 2

Measurement items.

CONSTRUCTS	ITEMS	INSTRUMENT	SOURCES
Perceived ease of use	PEOU1	I find it easy to master and use AIGC technology in teaching activities	(Davis, 1989)
	PEOU2	The user interface of AIGC technology is intuitive and easy for me
	PEOU3	Using AIGC technology in teaching requires little mental effort
Perceived usefulness	PU1	Using AIGC technology in teaching makes it easier for me to create instructional materials	(Davis, 1989)
	PU2	Using AIGC technology has enhanced my teaching skills and educational knowledge
	PU3	AIGC technology can save my time and improve efficiency in teaching
Attitude	AT1	AIGC technology makes teaching more interesting	(Teo et al., 2009)
	AT2	Teaching with AIGC technology is fun
	AT3	I like using AIGC technology
Satisfaction	SAT1	I am satisfied with my experience using AIGC technology in education	(Spreng & Olshavsky, 1993)
	SAT2	I am satisfied with the functions of AIGC technology in education
	SAT3	I am satisfied with the overall use of AIGC technology in education
Continuance intention to use	CITU1	I intend to use AIGC technology frequently in my future teaching	(Bhattacherjee, 2001)
	CITU2	I plan to use AIGC technology regularly in my teaching practice in the future
	CITU3	I would strongly recommend AIGC technology in education to others
Flow experience	FLO1	Using AIGC technology in education makes me fully immersed in the activity.	(Jackson & Marsh, 1996)
	FLO2	When using AIGC in education, I become so engaged that I lose track of time
	FLO3	When I use AIGC in teaching, I feel deeply focused and absorbed
Confirmation	CON1	AIGC technology in education performed better than expected	(Bhattacherjee, 2001)
	CON2	AIGC technology in education is more interesting than I expected
	CON3	AIGC technology in education met my expectations

Table 3

Measurement model assessment.

CONSTRUCTS	ITEMS	LOADINGS	CR	AVE
Perceived ease of use	PEOU1	0.895	0.896	0.742
	PEOU2	0.840
	PEOU3	0.847
Perceived usefulness	PU1	0.857	0.895	0.739
	PU2	0.840
	PU3	0.881
Attitude	AT1	0.884	0.915	0.781
	AT2	0.897
	AT3	0.871
Satisfaction to use	SAT1	0.922	0.944	0.850
	SAT2	0.924
	SAT3	0.920
Continuance intention to use	CITU1	0.901	0.935	0.828
	CITU2	0.907
	CITU3	0.922
Flow experience	FLO1	0.795	0.894	0.739
	FLO2	0.911
	FLO3	0.868
Confirmation	CON1	0.923	0.938	0.835
	CON2	0.899
	CON3	0.920

Table 4

Discriminant validity (HTMT).

DISCRIMINANT VALIDITY (HTMT)
	CITU	CON	FLO	AT	PEOU	PU	SAT
CITU
CON	0.719
FLO	0.589	0.581
AT	0.684	0.724	0.467
PEOU	0.198	0.355	0.092	0.409
PU	0.621	0.640	0.413	0.784	0.375
SAT	0.669	0.573	0.560	0.623	0.202	0.579

Table 5

CFA model fit indices.

FIT INDEX	VALUE	RECOMMENDED THRESHOLD	EVALUATION
χ²	385.44	–	–
df	168	–	–
χ²/df	2.29	<3.0	Excellent
CFI	0.964	>0.95	Excellent
TLI	0.955	>0.95	Excellent
RMSEA	0.055	≤0.06	Close fit
SRMR	0.053	<0.08	Good

[i] Note: CFI = Comparative Fit Index; TLI = Tucker-Lewis Index; RMSEA = Root Mean Square Error of Approximation; SRMR = Standardized Root Mean Square Residual.

Table 6

Coefficient of determination.

ENDOGENOUS VARIABLES	R SQUARE
CITU	0.518
AT	0.500
PU	0.328
SAT	0.336

Table 7

Outer VIF results.

VARIABLES	ITEMS	VIF
CITU	CITU1	2.511
	CITU2	2.735
	CITU3	3.076
CON	CON1	3.098
	CON2	2.516
	CON3	3.042
FLO	FLO1	2.225
	FLO2	2.977
	FLO3	1.711
AT	AT1	2.062
	AT2	2.426
	AT3	2.149
PEOU	PEOU1	2.185
	PEOU2	1.892
	PEOU3	1.727
PU	PU1	1.836
	PU2	1.767
	PU3	2.008
SAT	SAT1	3.134
	SAT2	3.224
	SAT3	2.953

Table 8

Results of hypotheses testing.

HYPOTHESES	RELATIONSHIPS	PATH COEFFICIENTS	T VALUES	P VALUES	INNER VIF	F²	EFFECT SIZE	DECISION
H1	CON→PU	0.505	10.935	0.000	1.106	0.343	Large	Supported
H2	CON→SAT	0.349	6.341	0.000	1.440	0.128	Medium	Supported
H3	FLO→CITU	0.223	5.196	0.000	1.387	0.075	Medium	Supported
H4	FLO→AT	0.220	5.559	0.000	1.154	0.084	Medium	Supported
H5	AT→CITU	0.262	4.457	0.000	2.060	0.069	Medium	Supported
H6	PEOU→AT	0.161	4.405	0.000	1.109	0.047	Small	Supported
H7	PEOU→PU	0.156	3.507	0.000	1.106	0.033	Small	Supported
H8	PU→CITU	0.139	2.610	0.009	1.867	0.021	Small	Supported
H9	PU→AT	0.530	12.652	0.000	1.270	0.442	Large	Supported
H10	PU→SAT	0.309	5.708	0.000	1.440	0.100	Medium	Supported
H11	SAT→CITU	0.280	5.457	0.000	1.693	0.096	Medium	Supported

Table 9

Testing model fit.

PARAMETER	SATURATED MODEL	ESTIMATED MODEL
SRMR	0.053	0.080
NFI	0.839	0.834

Table 10

Analysis of necessary condition for CITU.

	HIGH CITU		LOW CITU
ANTECEDENTS	CONSISTENCY	COVERAGE	CONSISTENCY	COVERAGE
PEOU	0.903	0.621	0.784	0.711
~PEOU	0.579	0.670	0.582	0.888
PU	0.911	0.701	0.680	0.692
~PU	0.600	0.587	0.707	0.913
AT	0.914	0.740	0.642	0.686
~AT	0.612	0.564	0.757	0.921
SAT	0.782	0.845	0.500	0.712
~SAT	0.733	0.526	0.891	0.844
FLO	0.690	0.860	0.443	0.729
~FLO	0.782	0.516	0.914	0.795
CON	0.896	0.797	0.575	0.675
~CON	0.634	0.531	0.827	0.913

Table 11

Sufficient configurations for CITU and ~CITU.

	CITU				~CITU
CONFIGURATION	PATH 1	PATH 2	PATH 3		PATH 1	PATH 2
PEOU			⦁	~PEOU	•
PU	⦁	•		~PU	•	•
AT	•	⦁	•	~AT	⦁	•
SAT	•		•	~SAT	•	•
FLO		•	⦁	~FLO	•	•
CON	•	⦁	•	~CON		⦁
Consistency	0.933	0.945	0.967		0.825	0.810
Raw coverage	0.705	0.632	0.573		0.005	0.005
Unique coverage	0.122	0.049	0.009		0.003	0.003
Overall consistency	0.918				0.815
Overall coverage	0.764				0.009

[i] Note: •denotes the presence of a condition, ⦁denotes the marginal presence of a condition. Blank spaces indicate the condition may be either present or absent.

Raw coverage = outcome share explained by a configuration, unique coverage = outcome share exclusively explained by a configuration.

References

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Articles in this issue

DOI: https://doi.org/10.55982/openpraxis.18.1.1010 | Journal eISSN: 2304-070X

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Language: English

Page range: 27 - 51

Submitted on: Sep 7, 2025

Accepted on: Oct 14, 2025

Published on: Feb 24, 2026

Published by: International Council for Open and Distance Education (ICDE)

In partnership with: Paradigm Publishing Services

Keywords:

Preschool teachers,

preschool education,

technology acceptance model,

continuous intention,

artificial intelligence generated content (AIGC),

technology acceptance model (TAM),

expectation confirmation model (ECM),

flow experience,

partial least squares structural equation modeling (PLSSEM),

fuzzy-set qualitative comparative analysis (fsQCA),

early childhood education,

perceived usefulness

© 2026 Yuxin Zhang, published by International Council for Open and Distance Education (ICDE)
This work is licensed under the Creative Commons Attribution 4.0 License.

Volume 18 (2026): Issue 1

Factors Influencing Preschool Teachers’ Continuous Intention to Use AI Generated Content in Education

Figures & Tables

Table 1