What Drives Students' Use of AI Chatbots? Technology Acceptance in Conversational AI

Griffin Pitts; Sanaz Motamedi

cs.HCFeb 24, 2026

What Drives Students' Use of AI Chatbots? Technology Acceptance in Conversational AI

Authors: Griffin Pitts, Sanaz Motamedi

Organizations: North Carolina State University, Raleigh, North Carolina, USA · Pennsylvania State University, University Park, Pennsylvania, USA

Abstract

Conversational AI tools have been rapidly adopted by students and are becoming part of their learning routines. This widespread use comes amid ongoing concerns about the reliability of AI-generated responses and the appropriate role of AI in education. Drawing on the Technology Acceptance Model, we examine how students' perceptions of conversational AI, including its usefulness, ease of use, trustworthiness, enjoyment, and social influence, relate to their intentions to use these tools for learning. We investigate both the direct and indirect relationships among these perceptions to understand how they are associated with students' behavioral intentions. Using survey data from 229 students and partial least squares structural equation modeling, we found that perceived usefulness was the strongest direct predictor of intention, followed by perceived enjoyment. Perceived ease of use, trust, and subjective norms did not have significant direct associations with intention. However, trust and subjective norms had significant total indirect associations, while perceived enjoyment had both significant direct and indirect associations. The model explained 70.2% of the variance in behavioral intention. These findings suggest that students' intentions to use conversational AI are associated less directly with effort-related considerations and more with perceived usefulness and affective experience. They also highlight the importance of understanding what students perceive as valuable in conversational AI interactions, particularly when designing tools intended to encourage engagement while supporting learning.

Figures & tables

Figure 1. Proposed theoretical framework and hypotheses for students’ adoption of conversational AI chatbots in education.

	BI	PU	PEOU	Trust	SN	PE
BI	0.928	0.799	0.672	0.715	0.577	0.733
PU	0.799	0.884	0.746	0.827	0.615	0.758
PEOU	0.672	0.746	0.828	0.628	0.451	0.623
Trust	0.715	0.827	0.628	0.900	0.582	0.716
SN	0.577	0.615	0.451	0.582	0.885	0.560
PE	0.733	0.758	0.623	0.716	0.560	0.893

Table 1. Fornell–Larcker matrix. Diagonal entries (bold) are the square roots of AVE; off-diagonal entries are construct correlations.

Construct Pair	HTMT	95% CI
PE – BI	0.772	[0.692, 0.840]
PEOU – BI	0.715	[0.611, 0.805]
PEOU – PE	0.663	[0.560, 0.751]
PU – BI	0.858	[0.794, 0.911]
PU – PE	0.819	[0.753, 0.875]
PU – PEOU	0.825	[0.744, 0.898]

Table 2. HTMT ratios and 95% confidence intervals.

Path	VIF	95% CI
PE $\rightarrow$ BI	2.695	[2.167, 3.365]
PE $\rightarrow$ PEOU	2.244	[1.841, 2.771]
PE $\rightarrow$ PU	2.244	[1.841, 2.771]
PEOU $\rightarrow$ BI	2.372	[1.921, 2.954]
PU $\rightarrow$ BI	5.201	[4.042, 6.601]
SN $\rightarrow$ BI	1.749	[1.435, 2.149]

Table 3. Inner VIF values with 95% confidence intervals.

Hyp.	Path	$\beta$	$p$ value	Result
H1	PU $\rightarrow$ BI	0.385	$<.001$	Supported
H2	PEOU $\rightarrow$ BI	0.142	.069	Not supported
H3	PEOU $\rightarrow$ PU	0.301	$<.001$	Supported
H4	Trust $\rightarrow$ BI	0.077	.288	Not supported
H5	Trust $\rightarrow$ PU	0.428	$<.001$	Supported
H6	Trust $\rightarrow$ PEOU	0.354	$<.001$	Supported

Table 4. Hypothesis testing results.

Path	$f^{2}$	SD	$t$	$p$
PE $\rightarrow$ BI	0.084	0.053	1.430	0.153
PE $\rightarrow$ PEOU	0.105	0.051	1.875	0.061
PE $\rightarrow$ PU	0.172	0.060	2.731	0.006
PEOU $\rightarrow$ BI	0.039	0.036	0.850	0.396
PU $\rightarrow$ BI	0.108	0.059	1.606	0.108
SN $\rightarrow$ BI	0.024	0.023	0.714	0.475

Table 5.

f^{2}

Effect Sizes

Path	$\beta$	SD	$t$	$p$
PE $\rightarrow$ BI	0.163	0.040	4.090	$<.001$
SN $\rightarrow$ BI	0.483	0.046	10.538	$<.001$
SN $\rightarrow$ PEOU	0.395	0.053	7.383	$<.001$
SN $\rightarrow$ PU	0.477	0.049	9.759	$<.001$
Trust $\rightarrow$ BI	0.256	0.050	5.086	$<.001$

Table 6. Total Indirect Effects

Figure 2. PLS-SEM structural model results with standardized path coefficients; solid lines denote significant paths and dashed lines denote non-significant paths (*p < .05, **p < .01, ***p < .001).

Appendix figures & tables2 assets

Supplementary material from the paper’s appendix.

Appendix

Construct	Item	Question Text	Reference
Behavioral Intention (BI)	BI1	I would use an AI chatbot while I am learning.	Adapted from Venkatesh & Davis (2000)
	BI2	I plan to use an AI chatbot while learning in the future.
	BI3	If given permission to use an AI chatbot for an academic course, I would use it.
	BI4	If given permission to use an AI chatbot for an academic course, I predict that I would use it.
Perceived Usefulness (PU)	PU1	An AI chatbot would be a useful tool to have while learning.	Adapted from Davis et al. (1989)
	PU2	An AI chatbot would decrease my frustration while learning.

Appendix

Table 9

Construct	Item	Factor Loading	M (SD)	Cronbach’s $\alpha$	CR	AVE
Behavioral Intention (BI)	BI1	.925	3.72 (1.13)	.947	.961	.862
	BI2	.919	3.73 (1.19)
	BI3	.930	3.91 (1.11)
	BI4	.940	3.85 (1.15)
Perceived Usefulness (PU)	PU1	.915	3.82 (1.08)	.906	.934	.781
	PU2	.830	3.46 (1.15)

Appendix

Table 10

Conversational Agents

Conversational agents are AI systems that interact with people through multi-turn dialogue to answer questions, provide support, or complete tasks.

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Exploring the Adoption Intention in Using AI-Enabled Educational Tools Among Preservice Teachers in the Philippines: A Partial-Least Square Modeling

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This study examines the factors influencing pre-service teachers' behavioral intention to use AI-enabled educational tools during their practicum, using the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) as the theoretical framework. The model includes the core UTAUT2 constructs such as performance expectancy, effort expectancy, hedonic motivation, social influence, facilitating conditions, price value, and habit. It also incorporates additional predictors including computer self-efficacy, computer anxiety, and computer playfulness. Data were collected from 563 pre-service teachers using a structured questionnaire and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The results indicate that performance expectancy and hedonic motivation are the strongest predictors of behavioral intention. Computer self-efficacy, computer anxiety, and computer playfulness significantly influenced effort expectancy, although effort expectancy did not directly predict behavioral intention. Performance expectancy was significantly predicted by extrinsic motivation, job fit, relative advantage, and outcome expectations. Constructs such as social influence and facilitating conditions showed limited or inverse effects. These findings suggest that internal motivational, cognitive, and emotional factors are more influential than external or institutional factors in shaping the adoption of AI-enabled tools. The study highlights the importance of promoting personal relevance, confidence, and enjoyment in teacher preparation programs to encourage technology integration.

Abstract

Conversational Agents

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