ICTMT 2025 | International Journal of Research and Innovation in Social Science (IJRISS)

ISSN: 2454-6186 | DOI: 10.47772/IJRISS

Special Issue | Volume IX Issue XXVIII November 2025

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Determinants of Students’ Intention to Use AI Chatbots in

Entrepreneurship Education: A Technology Acceptance Model

Perspective

Siti Nur Aisyah Alias

, Atirah Sufian

, Mohd Fazli Mohd Sam

, Mohd Fauzi Kamarudin

, Norhidayah

Jamaluddin

, Thahira Bibi TKM Thangal

Fakulti Pengurusan Teknologi dan Teknousahawanan, Universiti Teknikal Malaysia Melaka

Faculty of Business Management, Universiti Teknologi Mara, Johor, Malaysia

Corresponding Author

DOI: https://dx.doi.org/10.47772/IJRISS.2025.92800045

Received: 07 November 2025; Accepted: 14 November 2025; Published: 20 December 2025

ABSTRACTS

Entrepreneurship is central to achieving the United Nations’ Sustainable Development Goals (SDGs) and

Malaysia’s national strategies to create jobs, foster innovation, and encourage youth entrepreneurship.

Supporting this vision, Malaysian universities play a vital role in developing entrepreneurial mindsets, with AI

chatbots emerging as innovative tools in teaching and learning. This study investigates students’ intention to

use AI chatbots in entrepreneurship education across the Malaysian Technical University Network (MTUN),

using the Technology Acceptance Model (TAM) extended with perceived trust. Survey data from 377 students

revealed that perceived ease of use, usefulness, and trust significantly influence intention, explaining 71% of

the variance, with ease of use being the strongest predictor. The findings highlight the importance of user-

friendly, trustworthy, and effective chatbot systems to support entrepreneurial education and align with

national development goals.

Keywords: AI Chatbots, Entrepreneurship Education, Technology Acceptance Model (TAM), Perceived Trust,

Students’ Intention

INTRODUCTION

Entrepreneurship education has emerged as a critical driver of innovation, economic resilience, and societal

progress in the 21st century. The Ministry of Higher Education Malaysia (MOHE, 2023) underscores its rapid

evolution, propelled by technological advancements and the growing recognition of entrepreneurship as a

catalyst for sustainable growth. Globally, the educational technology market is projected to reach USD 404

billion by 2025, with a Compound Annual Growth Rate (CAGR) of 16.3% (HolonIQ, 2022), reflecting a

paradigm shift in how education is delivered and consumed. Among these technologies, artificial intelligence

(AI) has been identified as a transformative force, particularly through the integration of AI chatbots that are

redefining traditional pedagogical practices.

In Malaysia, the integration of AI in higher education has gained remarkable traction. A survey conducted by

the Malaysian Digital Economy Corporation (MDEC, 2023) reported that 75% of universities have begun

adopting AI-driven tools, including chatbots, to enrich learning experiences and strengthen student

engagement. These developments are aligned with the government’s National Artificial Intelligence Roadmap

2021-2025, which positions AI as a strategic enabler across sectors, including education (MDEC, 2021). Such

initiatives demonstrate Malaysia’s commitment to preparing students for the digital economy while

simultaneously advancing its entrepreneurial ecosystem.

ICTMT 2025 | International Journal of Research and Innovation in Social Science (IJRISS)

ISSN: 2454-6186 | DOI: 10.47772/IJRISS

Special Issue | Volume IX Issue XXVIII November 2025

Page 452

www.rsisinternational.org

Scholarly evidence supports the potential of AI chatbots in education. Studies highlight their ability to provide

personalized learning pathways, real-time feedback, and access to vast resources (Ratten & Jones, 2021), with

the AI in education market expected to grow at a CAGR of 23.3% between 2021 and 2026

(MarketsandMarkets, 2023). In the Malaysian context, this growth trajectory aligns with national policies such

as the Entrepreneur Development Policy 2030, which targets the creation of 100,000 new entrepreneurs, and

the National Entrepreneurship Policy 2030, which emphasizes innovation-led learning environments. The

integration of AI chatbots into entrepreneurship education is therefore not only a technological enhancement

but also a strategic mechanism to foster creativity, self-efficacy, and entrepreneurial intention among students

(Shabbir, Batool & Mahmood, 2022).

Despite this promise, the behavioral intention to use AI chatbots in higher education, particularly in

entrepreneurship education remains underexplored. Previous studies (e.g., Kim, Park, & Kim, 2021; Tan, Lim

& Ng, 2022) indicate that intention to adopt AI technologies is shaped by perceived ease of use, perceived

usefulness, perceived trust and institutional support. However, limited empirical research has examined these

factors within the Malaysian higher education context, where the entrepreneurial agenda is closely tied to

national development goals. Understanding how students, particularly those in technical universities, perceive

and are willing to adopt AI chatbots for entrepreneurship education is therefore both timely and crucial.

Building on this concern, artificial intelligence (AI) has increasingly transformed industries and educational

landscapes, offering intelligent systems capable of learning, adapting, and supporting decision-making (Russell

& Norvig, 2016). Nevertheless, while AI technologies have long enhanced commercial practices, their

application in entrepreneurship is still emerging, with scholars noting both opportunities and theoretical

challenges for entrepreneurial practice (Townsend et al., 2018; Obschonka & Audretsch, 2020). Despite AI’s

recognized potential, its integration into entrepreneurship education, especially through AI chatbots, remains

underexplored. Existing studies have primarily examined AI in broader educational contexts (Clarizia et al.,

2018; Agarwal et al., 2022), thereby leaving a significant gap in understanding how AI chatbots can effectively

foster entrepreneurial learning, competencies, and intentions.

This gap is especially pronounced in Malaysia, where AI adoption has become a national priority under the

National Artificial Intelligence Roadmap 2021-2025 and the MTUN Advanced TVET 2030 initiative. These

policies seek to elevate Technical and Vocational Education and Training (TVET) and entrepreneurship

education to parity with mainstream academic pathways, equipping students not only with technical expertise

but also entrepreneurial and soft skills (The Star, 2024). Within this agenda, the Malaysian Technical

University Network (MTUN) has strategically introduced AI chatbots into its curriculum, positioning them as

tools to deliver personalized learning experiences, real-time feedback, and innovation-driven outcomes.

However, while such adoption reflects institutional commitment, little is known about how students perceive,

accept, and intend to use AI chatbots in entrepreneurship education.

Understanding these factors is critical. According to the Technology Acceptance Model (Davis, Bagozzi &

Warshaw, 1989), perceptions of usefulness, ease of use, and trust strongly influence technology adoption. Yet,

within the context of entrepreneurship education in Malaysia, empirical evidence on these determinants

remains scarce. Moreover, Amara’s Law (Amara, 1984) cautions against overestimating short-term capabilities

of AI while underestimating its long-term impact, underscoring the need for realistic insights into students’

expectations and readiness.

Therefore, a pressing problem emerges despite global enthusiasm for AI in education and Malaysia’s proactive

adoption of AI-driven tools, there is insufficient empirical research on the behavioral intention of students to

use AI chatbots in entrepreneurship education. Without such understanding, universities risk misaligning

technological investments with learners’ needs, thereby limiting the effectiveness of AI in strengthening

entrepreneurial competencies. Addressing this problem is essential to inform educational strategies, support

national policy goals, and prepare future entrepreneurs for success in the digital economy.

Accordingly, this study investigates the factors influencing the intention to use AI chatbots in entrepreneurship

education among students with a focus on Universiti Malaysia Pahang Al-Sultan Abdullah (UMP), Universiti

Tun Hussein Onn Malaysia (UTHM), Universiti Teknikal Malaysia Melaka (UTeM) and Universiti Malaysia

ICTMT 2025 | International Journal of Research and Innovation in Social Science (IJRISS)

ISSN: 2454-6186 | DOI: 10.47772/IJRISS

Special Issue | Volume IX Issue XXVIII November 2025

Page 453

www.rsisinternational.org

Perlis (UniMAP). Drawing on the Technology Acceptance Model (Davis, Bagozzi & Warshaw, 1989) and

extended constructs of trust and institutional support, the research aims to provide empirical insights into

students’ readiness to adopt AI-driven tools in their entrepreneurial learning processes. By doing so, it

contributes to the growing body of knowledge on AI adoption in education and offers practical implications for

policymakers and educators seeking to strengthen Malaysia’s entrepreneurial ecosystem in the digital era.

Entrepreneurship Education

Entrepreneurship education is rapidly expanding worldwide due to its ability to connect theory with real-world

practice. In fact, over 70% of universities are incorporating it into their curricula, reflecting the government’s

vision to strengthen the entrepreneurial ecosystem (MOHE, 2022). Typically, it is delivered through three

approaches: education "about" entrepreneurship, which provides theoretical foundations; education "for"

entrepreneurship, which develops students’ entrepreneurial skills; and education "through" entrepreneurship,

which emphasizes experiential learning through real-world projects. Moreover, Artificial Intelligence (AI),

particularly generative AI, has strong potential to transform entrepreneurship education, especially in the

"about" approach by generating and presenting information traditionally delivered by lecturers. This shift,

therefore, could redefine the educator’s role from knowledge transmission to facilitating deeper understanding

and application. Taken together, these approaches ensure students gain not only knowledge but also the skills

and experience needed to succeed in an evolving entrepreneurial landscape.

Building on this foundation, universities adopt experiential and problem-based learning, industry partnerships,

and entrepreneurship centers to build students’ creativity, problem-solving skills, and entrepreneurial self-

efficacy (Lee et al., 2023; Vivekananth et al., 2023). At the same time, national policies, including the

Entrepreneur Development Policy 2030 and the National Entrepreneurship Policy 2030, aim to nurture

100,000 new entrepreneurs and foster innovation. Consequently, Malaysia’s strong ranking in the Global

Entrepreneurship Index further highlights these efforts. Looking forward, the integration of innovative

technologies such as AI chatbots is expected to enhance students’ learning experiences and entrepreneurial

competencies.

In parallel, entrepreneurship education in Malaysia has rapidly expanded, with more than 70% of universities

embedding entrepreneurship courses, reflecting the government’s strategic commitment to strengthening the

entrepreneurial ecosystem (MOHE, 2022). Specifically, universities employ experiential, problem-based, and

industry-linked pedagogies that enhance students’ creativity, problem-solving, and entrepreneurial self-

efficacy, significantly increasing their intentions to start businesses (Ismail et al., 2021; Rahim & Isa, 2023).

Furthermore, supported by national strategies such as the Entrepreneur Development Policy 2030, which

targets 100,000 new entrepreneurs (Entrepreneur Development Policy, 2021), and initiatives like the Malaysian

Global Innovation & Creativity Centre (MaGIC, 2021), the country has built a robust entrepreneurship support

infrastructure. Additionally, Malaysia’s ranking in the Global Entrepreneurship Index (Statista.com, 2022) and

the establishment of Entrepreneurship Development Centers across universities (Lee, Tan & Low, 2023)

further underscore this progress. Ultimately, integrating innovative technologies such as AI chatbots is

expected to enrich entrepreneurial education and strengthen students’ competencies.

Intention to use Artificial Intelligence Chatbot

The intention to use AI chatbots is shaped by key factors from the Technology Acceptance Model (TAM),

including perceived usefulness, ease of use, trust, enjoyment, and social influence (Smith & Jones, 2023; Chen

et al., 2023; Lee & Park, 2023). Positive perceptions of efficiency, convenience, and reliability enhance user

attitudes, leading to higher adoption rates. For businesses, fostering trust and creating engaging, user-friendly

chatbot experiences is essential to strengthen user intentions, which in turn drive customer satisfaction, loyalty,

and overall performance.

The Technology Acceptance Model (TAM), developed by Davis (1989) and expanded from the Theory of

Reasoned Action, is widely used to explain users’ adoption of new technologies. Its core constructs: perceived

usefulness and perceived ease of use have been shown to significantly influence technology adoption, with

perceived trust increasingly recognized as an additional determinant (Ayanwale & Molefi, 2024). Studies

ICTMT 2025 | International Journal of Research and Innovation in Social Science (IJRISS)

ISSN: 2454-6186 | DOI: 10.47772/IJRISS

Special Issue | Volume IX Issue XXVIII November 2025

Page 454

www.rsisinternational.org

further highlight the role of individual, social, and environmental factors in shaping user intentions (Teo et al.,

2021). As such, TAM provides a robust framework for examining the relationship between predictors and

intention to use AI in educational settings.

Perceived ease of use (PEOU) is a key determinant of AI chatbot adoption in entrepreneurship education. It

reflects how simple users find the technology to operate (Davis, 1989) and strongly influences attitudes and

intentions toward usage. Studies show that when students view chatbots as intuitive and user-friendly, they are

more likely to integrate them into learning (Chang & Wu, 2023; Liu & Zhou, 2022). PEOU also mediates the

relationship between attitudes and behavioral intentions (Wang & Li, 2023), highlighting its critical role in

shaping acceptance. Ensuring ease of use is therefore essential for successful integration of AI chatbots in

Malaysian higher education.

Perceived usefulness (PU) is a central factor influencing students’ acceptance of AI chatbots in

entrepreneurship education. Defined as the belief that a system enhances performance (Zulfiqar et al., 2021),

PU has been consistently shown to predict usage intentions. Studies highlight that accurate, reliable, and

beneficial chatbot functionalities increase students’ satisfaction and intention to adopt them (Almahri et al.,

2020; Malik et al., 2021). When students view chatbots as tools that support academic goals through instant

access, personalized support, and interactive learning, they develop positive attitudes toward their use. In line

with the Technology Acceptance Model (TAM), PU strongly drives the adoption of AI chatbots in Malaysian

higher education.

Perceived trust is a key determinant in the adoption of AI chatbots, reflecting users’ confidence in the

technology’s credibility, reliability, and security (Liden & Nilros, 2020; Winkler & Soellner, 2018). Research

shows that students are more likely to use chatbots when they trust their accuracy, dependability, and ability to

safeguard personal data (Gallimore et al., 2019; Pillai et al., 2023). In entrepreneurship education, trust

enhances students’ motivation to engage with chatbots, particularly when sharing personal information for

tailored support. Building trust requires reliable performance, clear responses, secure data handling, and

advanced conversational capabilities. Overall, trust plays a pivotal role in fostering acceptance and effective

use of AI chatbots in educational settings.

METHODOLOGY

This study employed a quantitative research design to investigate the factors influencing university students’

intention to use artificial intelligence (AI) chatbots in entrepreneurship education. The Technology Acceptance

Model (TAM) served as the theoretical framework, with three independent variables (perceived ease of use,

perceived usefulness, and perceived trust) and one dependent variable (intention to use AI chatbots). A cross-

sectional survey method was selected to collect data from students at Malaysian Technical University Network

(MTUN).

The target population consisted of undergraduate students enrolled at MTUN, as they represent the primary

group exposed to entrepreneurship-related courses and emerging digital learning tools. A simple random

sampling technique was applied to ensure equal probability of selection and minimize sampling bias. Based on

Krejcie and Morgan’s (1970) sampling table, a sample size of 377 students was deemed sufficient to represent

the population and provide statistical validity. Moreover, random sampling was aimed to look at the three

elements of the sample respondents, namely types of MTUN institution, the years of study and engineering

students with entrepreneurship education experiences. The selection of these elements was based on the data

requirements for this research. Typically, this type of sampling is biased. However, the findings of the research

using sampling are not representative or descriptive of the population, but rather provide an initial image of the

field of study (Fruth et al., 2019). The result of the sample information obtained in this study are as shown in

Table 1 below.

Table 1: Respondents’ Sample

No.

MTUN Institution

Location

Number of samples

Universiti Tun Hussein Onn Malaysia

Johor

103

ICTMT 2025 | International Journal of Research and Innovation in Social Science (IJRISS)

ISSN: 2454-6186 | DOI: 10.47772/IJRISS

Special Issue | Volume IX Issue XXVIII November 2025

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(UTHM)

Universiti Teknikal Malaysia Melaka (UTeM)

Melaka

101

Universiti Malaysia Pahang (UMP)

Pahang

Universiti Malaysia Perlis (UniMAP)

Perlis

Instrument Design

Data were collected using a structured questionnaire comprising three main sections:

Section

Variables

Items

Demographic Information

Independent Variable 1: Perceived Ease of Use (PEOU)

Independent Variable 2: Perceived Usefulness (PU)

Independent Variable 3: Perceived Trust (PT)

Dependent Variable: Intention to Use Artificial Intelligence Chatbot

The questionnaire used in this study was adapted from Ayanwale and Molef's (2024) and Yap and Kamaruddin

(2023) research. While their original framework provided a foundation, modifications were made to align the

questions with the specific context and objectives of this research. This approach ensured that the

questionnaire was both relevant to the research topic and suitable for the target audience, enhancing the

validity of the data collected. All items were adapted from validated scales (Davis, 1989; Gefen et al., 2003)

and measured on a five-point Likert scale (1 = Strongly Disagree to 5 = Strongly Agree). Data were collected

over six weeks (March–April 2024) through online distribution via university learning platforms and official

student networks, ensuring voluntary participation and confidentiality.

Pilot Test

A pilot study with 38 respondents (10% of the total sample) confirmed clarity and reliability. Cronbach’s alpha

values ranged between 0.745 and 0.844, indicating acceptable to very good reliability (see Table 2).

Table 2: Pilot Test’s Reliability Statistics

Variables

Cronbach’s

alpha

Reliability

Independent Variable 1: Perceived Ease of Use (PEOU)

0.775

Good

Independent Variable 2: Perceived Usefulness (PU)

0.830

Very Good

Independent Variable 3: Perceived Trust (PT)

0.745

Good

Dependent Variable: Intention to Use Artificial Intelligence Chatbot

0.844

Very Good

The table presents the results of the reliability test for the pilot study. The highest Cronbach’s alpha value,

0.844, was found for the scale measuring the intention to use artificial intelligence chatbots. On the other hand,

the lowest Cronbach’s alpha value of 0.745 was for the perceived trust (PT) scale. All the scales were deemed

reliable, as the Cronbach’s alpha values for all variables were greater than 0.6.

RESULTS AND DISCUSSION

In terms of gender, most respondents are female students which totals up to 233 people (61.8%) and the rest

are male students which totals up to 144 people (38.2%). In terms of MTUN institutions participation, the

ICTMT 2025 | International Journal of Research and Innovation in Social Science (IJRISS)

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majority of respondents are from Universiti Tun Hussein Onn Malaysia (UTHM) with 103 students (27.3%),

followed by Universiti Teknikal Malaysia Melaka (UTeM) with 101 students (26.8%), Universiti Malaysia

Pahang (UMP) with 98 students (26%) and Universiti Malaysia Perlis (UniMAP) with 75 students (19.9%).

Year 4 students dominate the number of respondents with 113 students (30%), followed by year 2 students

with 111 students (29.4%), year 1 students with 93 students (24.7%) and year 3 students with 60 students

(15.9%).

Table 3: Model Summary of Multiple Regression Analysis

Model Summary

Model

R Square

Adjusted R Square

Std. Error of the

Estimate

0.847

0.717

0.715

1.77094

1. Predictors: (Constant), PEOU, PU, PT, ITU

2. Dependent Variable: (DV)

The multiple regression model summary revealed a strong relationship between the variables, as the value is

greater than 0.5. The independent variables, Perceived Ease of Use (PEOU), Perceived Usefulness (PU), and

Perceived Trust (PT) were found to be relevant and closely related to the dependent variable, which is the

intention to use artificial intelligence chatbots. The R-squared value of 0.717 suggests that 71% of the variation

in the dependent variable can be explained by the three independent variables. The Adjusted R Square of

0.847, which accounts for the number of predictors and the sample size, further confirmed the robustness and

good fit of the model. The standard error of the estimate (1.77094) suggested a relatively low average

deviation between the observed and predicted intention to use AI Chatbot scores, indicating a reasonably

accurate model. Collectively, these results strongly suggested that the chosen independent variables provided a

compelling explanation for the variations observed in intention to use AI Chatbot in Entrepreneurial Education

among MTUN students.

Table 4: Regression Analysis on ANOVA

ANOVA

Model

Sum of Squares

Mean Square

Sig.

Regression

2965.624

988.541

315.201

<0,001

Residual

1169.813

373

3.136

Total

4135.438

376

1. Dependent Variable: DV

2. Predictors: (Constant), PT, PEOU, PU

The ANOVA analysis further supported the significance of the regression model in explaining the variation in

intention to use AI Chatbot (F = 315.201, p < 0.001). With three predictors (Perceived Ease of Use, Perceived

Usefulness, and Perceived Trust), the substantially larger regression sum of squares (2965.624) compared to

the residual sum of squares (1169.813) clearly demonstrated that the model explained a significant portion of

the variance in the dependent variable. The high F-value, derived from the mean square for regression

(988.541) and the mean square for residual (3.136), unequivocally confirmed the strong explanatory power of

the model. This statistical significance underscored the crucial role of Perceived Ease of Use, Perceived

Usefulness, and Perceived Trust in intention to use AI Chatbot in Entrepreneurial Education among MTUN

students.

ICTMT 2025 | International Journal of Research and Innovation in Social Science (IJRISS)

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Table 5: Regression Analysis on Coefficients

Coefficients

Model

Unstandardized

Coefficients

Standardized

Coefficients

Sig.

Std. Error

Beta

(Constant)

2.382

0 .600

3.967

<0.001

PEOU

0.539

0.063

0.437

8.550

<0.001

0.285

0.064

0.234

4.444

<0.001

0.296

0.039

0.274

7.584

<0.001

1. Dependent Variable: DV

The coefficients for the regression analysis, presented in Table 3, indicated the individual contribution of each

predictor variable to intention to use AI Chatbot in Entrepreneurial Education. Perceived Ease of Use exhibited

the highest positive beta value (β = 0.437, p < 0.001), signifying that it had the most substantial positive effect

on intention to use AI Chatbot compared to the other two variables. Perceived Trust (β = 0.274, p < 0.001) and

Perceived Usefulness (β = 0.234, p < 0.001) also demonstrated significant positive beta values, indicating their

important contributions to the dependent variable. These results suggested that while all three independent

variables were significant predictors of intention to use AI Chatbot.

The p-values for all hypothesized relationships were below the critical threshold of 0.05, confirming their

statistical significance and supporting the acceptance of all proposed hypotheses. The results clearly indicate

that perceived ease of use, perceived usefulness, and perceived trust each exhibit a significant positive

relationship with the intention to use AI chatbots in entrepreneurial education. These findings highlight that the

three factors are valid, reliable, and impactful determinants, underscoring their critical role in fostering the

adoption of AI chatbot technology.

CONCLUSION

The findings of this study corroborate the growing global recognition of the importance of technology

adoption in advancing educational practices, particularly within entrepreneurial education. The coefficients for

the regression analysis, presented in Table 3, indicated the individual contribution of each predictor variable to

the intention to use AI chatbots in entrepreneurial education. Perceived ease of use exhibited the highest

positive beta value (β = 0.437, p < 0.001), signifying that it had the most substantial effect on intention to use

AI chatbots compared to the other two variables. Perceived trust (β = 0.274, p < 0.001) and perceived

usefulness (β = 0.234, p < 0.001) also demonstrated significant positive beta values, indicating their important

contributions to the dependent variable. These results suggest that while all three independent variables are

significant predictors of intention to use AI chatbots, perceived ease of use emerges as the most influential

factor in driving adoption.

The p-values for all hypothesized relationships were below the critical threshold of 0.05, confirming their

statistical significance and supporting the acceptance of all proposed hypotheses. Descriptive analysis further

supported these findings, with respondents expressing positive perceptions regarding the simplicity of

interacting with AI chatbots, their trust in the reliability of the technology, and the usefulness of chatbots as a

learning tool. This alignment between descriptive and inferential results underscores the argument that AI

chatbots hold considerable potential to enhance both teaching and learning experiences in entrepreneurial

education.

Collectively, this study provides robust empirical evidence of the significant influence of perceived ease of

use, perceived usefulness, and perceived trust on the adoption of AI chatbot technology in entrepreneurial

education. Perceived ease of use plays a particularly critical role by lowering barriers to adoption and

ICTMT 2025 | International Journal of Research and Innovation in Social Science (IJRISS)

ISSN: 2454-6186 | DOI: 10.47772/IJRISS

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increasing willingness to integrate technology into learning activities. Perceived usefulness reinforces adoption

by demonstrating the practical benefits of AI chatbots in improving learning outcomes, while perceived trust

alleviates concerns about reliability, credibility, and privacy, thereby strengthening students’ confidence in

using the technology.

LIMITATION AND FUTURE RESEARCH

While this study offers valuable empirical insights, several limitations should be acknowledged. First, the

findings are limited in generalizability as the sample was drawn exclusively from MTUN institutions, which

may not fully represent the diversity of Malaysian higher education contexts. Future studies should extend to

public and private universities to strengthen external validity. Second, the cross-sectional design restricts

causal interpretations and does not capture temporal shifts in students’ attitudes toward AI. Longitudinal or

experimental research could track changes in perception and sustained adoption patterns over time. Third, the

quantitative-only approach provides breadth but not depth in understanding students’ lived experiences. Future

studies could adopt mixed-method designs, incorporating interviews or focus groups to capture richer

pedagogical and emotional dimensions of AI chatbot use. Finally, exploring moderating factors such as digital

literacy, learning culture, and institutional support could yield more comprehensive insights into AI integration

success.

Contribution

This study advances theory and practice in three significant ways. Theoretically, it validates the Technology

Acceptance Model (TAM) within the domain of AI-driven entrepreneurship education, confirming perceived

ease of use, usefulness, and trust as strong determinants of behavioral intention. Practically, the results suggest

that educators and technopreneurs should collaborate to co-design AI chatbot systems that are user-friendly,

context-sensitive, and pedagogically aligned. For example, educators could provide real-world

entrepreneurship scenarios, while technopreneurs develop adaptive AI algorithms that personalize guidance

and feedback. Such collaboration ensures that chatbot tools not only deliver content but also foster critical

thinking, creativity, and entrepreneurial self-efficacy. Managerially, institutions can leverage these findings to

design professional development programs that build educator confidence in using AI and to establish policies

ensuring ethical, transparent, and privacy-respecting AI deployment.

ACKNOWLEDGEMENT

The authors gratefully acknowledged the Sustainable IT-economics, Information Systems, Technology

Management & Technopreneurship (SuITE) research group of Center for Technopreneurship Development (C-

TeD), the financial support through the publication incentive and the Fakulti Pengurusan Teknologi dan

Teknousahawanan, Universiti Teknikal Malaysia Melaka. All errors and omissions are the responsibility of the

authors.

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