Dr. Riya A Yadav

Assistant Professor, Banarsidas Chandiwala Institute of Hotel Management & Catering Technology, New Delhi

 

Ms. Jyotsna

Assistant Professor, Banarsidas Chandiwala Institute of Hotel Management & Catering Technology, New Delhi

 

Dr. Rohit Kumar

Assistant Professor, Banarsidas Chandiwala Institute of Hotel Management & Catering Technology, New Delhi

 

 

ABSTRACT:

Purpose – This study aims to explore the perceptions and acceptance of AI technologies among hospitality Educators in India.

Design/Methodology/Approach- This research explores the extent to which hospitality Educators in India are familiar with and accessible to the incorporation of AI tools and applications into their daily teaching practices. The study adopted quantitative based approach using convenience sampling with 245 sample size.

Findings- The findings suggest that several Educators were familiar with the probable benefits of AI in enhancing educational experiences, however, also exhibit their concerns about the technological, pedagogical, and ethical challenges associated with its application. The study adopted the TAM model and provides valuable understandings into the current state of AI adoption in Indian higher education, and offers recommendations for effective strategies to support the responsible and sustainable integration of AI in hospitality education.

Originality- The entire research work is the original work of the authors wherein the Structural equation Modelling conducted to highlight the perception and acceptance of AI in education along with its potential challenges.

Research Limitations- The survey was limited to Delhi/NCR region, which further can be extended to other region for further study in future. 

Practical Implications:-This paper depicts that AI has the potential to significantly improve learning outcomes, increase student engagement, and increase teaching effectiveness in the hospitality sector.

 

Keywords: Artificial Intelligence In education, AI in hospitality education, Hospitality Educators perception towards AI in education

 

 

1. Introduction

Education in hospitality and tourism has grown significantly over the two decades, with a noteworthy uptick in the last twenty-five years of the twentieth century (Airey, 2015; E Goh, 2020).

Technology has disrupted the way that programs are taught and how students relate with hospitality education at this time of expansion (Fotiadis, 2019; Goh E. &., 2020; Goh E. N., 2017). The growth of related research is another sign of the growing interest in hospitality and tourism education (Airey, 2015; Daniel, 2017; Goh E. &., 2020). Even with these developments, studies on hospitality and tourism education still make up a moderately small portion of the field's total body of literature. The incorporation of Artificial Intelligence (AI) into educational practices signifies a transformative change with the high potential to noticeably enhance teaching and learning experiences within the students. Artificial intelligence technologies, including machine learning algorithms, natural language processing, and intelligent instructional systems, are progressively being utilized to customize learning, streamline administrative functions, and enhance educational results (Q Liu, 2020). In the context of hospitality education, which combines theoretical knowledge with practical skills, AI offers an innovative opportunities to generate quite effective and attractive learning platform.

 Recent studies have highlighted the possible benefits of Artificial Intelligence in education, including personalized learning experiences, personalized to specific students' needs, enhanced learning through interactive tools, and the ability to examine massive amounts of educational data for better decision-making (Cheng, 2019). However, the application of AI in educational settings, particularly within specialized fields such as hospitality, requires a careful understanding of how educators notice and accept these technologies. (Sharma S. &., 2024) Study highlighted an analysis of the use of AI-based Chabot’s by faculty members in Indian higher education, with a focus on the influence of engagement and performance. (Deri, 2024) Underlines the significance of including digital capabilities into hospitality school and its imperative for graduates to acquire new skills to sustain competitiveness. (Zhang, 2024) Examines how ChatGPT can enhance learning outcomes and experiences, encourage educational equality and efficiency, and raise ethical, technological, and pedagogical subjects related to hospitality and tourism education.

There is still a lack of knowledge regarding the particular attitudes and acceptance levels of Educators of hospitality, particularly in the Indian setting, despite the encouraging expansion of AI applications. The effective amalgamation of AI into hospitality education depends extensively on the attitudes and readiness of faculty members who will use these technologies in their teaching methods (Zawacki-Richter, 2019). Artificial Intelligence is providing promising grounds in all the industries and expanding its roots. AI has revolutionized and has completely transformed the educational landscape enabling one to experience more real time learning by creating simulations, personalized modules, enhancing visuals, adding more colors to the regular classroom teachings.

Artificial intelligence (AI) in hospitality education is a relatively current trend in India, and little is known about how hospitality Educators perceive and embrace AI tools in the classroom. Artificial Intelligence (AI) is a rapidly advancing technology, renowned for its significant role in reforming various aspects of modern life (Peters et al.,2015).Understanding these perceptions is important as Educators play a vital role in implementing AI-driven tools, directly impacting how well they improve educational results (Cheng, 2019). Understanding the difficulties and challenges Educators encounter can help develop solutions to the adoption of AI. The purpose of this study is to fill the gap in the literature by investigating how Indian hospitality educators perceive and adopt AI in the classroom. The results will help guide future educational practices and regulation as well as the successful integration of AI into hospitality courses. This study will complement to the broader discussion on AI in education by examining the relationship of the variables influencing academics' acceptance of the technology and offering practical suggestions for stakeholders.

(Sharma S. a., 2023) Brings attention to how rapidly technology, digitization, and sustainability are changing the hotel sector. The study questions whether the current curriculum fulfill industry demands and highlights the importance of industry-academia collaboration in assuring that graduates have the necessary skills and knowledge. Integrating Artificial Intelligence in curriculum has shown significant potential to increase learning outcomes, personalize instruction, and restructure administrative responsibilities (Cheng, 2019; KF Hew, 2010; Huang C. , 2020).

However, successful implementation depends extensively on educators’ acceptance and perception of these tools (O Zawacki-Richter, 2019). In India's rapidly evolving hospitality education sector, limited research explores how Educators perceive and accept AI. Understanding the perception on AI's benefits, challenges, and impact on education practices is important for effective integration. Without these understandings, Educational Institutions may come upon opposition or challenges when implementing AI-driven solutions that could improve educational outcomes (Liu, 2020). Therefore, this research focuses on identifying and analyzing factors influencing hospitality Educators acceptance and their perception on AI, addressing literature gaps, and providing understandings to simplify AI adoption in hospitality education. This paper aims to explore the perception and acceptance of the Educators in hospitality institutes/universities. Though AI is growing fasts and gaining popularity, also holds some challenges or difficulties in complete adoption of it in education sector.   

The objectives of the study are:

• To assess hospitality Educators' awareness and understanding of AI in education.
• To determine Educators' perceptions of the benefits and challenges of incorporating AI into hospitality education.
• To identify the factors that influences the acceptance of AI by hospitality Educators in India.

 

Fig 1.1 Proposed Model

 

2. Literature Review

Artificial Intelligence (AI) is becoming increasingly important in education (Chen X. X., 2020). Artificial intelligence in education refers to computers that carry out human-like reasoning tasks, particularly in learning and problem-solving. The goal of integrating intelligent teaching approaches, curriculum design, and course structure into the education sector over the past 30 years is to impart in students an awareness of environmental and sustainable development (ESD) while also integrating cutting-edge technologies like artificial intelligence within the ESD framework (Shishakly, 2024). Both students and Educators benefit significantly from the advancement of classroom mobility brought about by technology. Educational technology simplifies learning at whatever time and from choice of your location by allowing students to participate in tutorial activities while balancing other tasks (Davis N. G., 2019). Flexibility can lead to better-quality learning outcomes and understandings, along with increased student motivation and engagement (Huang Y. L., 2014; Knight, 2020). Moreover, due to this education technology educators can conveniently manage online content distantly (Davis N. G., 2019). Educational technology creates a feeling of community that improves student-teacher connection, networking, and collaboration (Goh E. &., 2020; Lee, 2016; McCarthy, 2012). According to (Miller, 2012), because blogs and wikis encourage participatory learning, students appreciate using them for class discussions. (Goh E. &., 2020) Findings strongly indicate that automated discussion boards ought to be incorporated into course curriculum due to their proven advantages.

AI is used in this integration to monitor student forums, conduct intelligent tests, act as a learning companion, support or replace Educators, and provide private tutoring. Additionally, AI-Ed advances science teaching by acting as a research instrument (Holmes, 2023). Previous studies have examined a number of characteristics of AI in Education (AI-Ed), including views toward the use of Chabot’s in the classroom, factors influencing students' sustained interest in AI learning, and educators' preparedness to teach AI (Li W. Z., 2024). Moreover, Research has observed at the factors that affect students' continued interest in learning AI (Chai, 2020), how they perceive AI coaching (Terblanche, 2023), and institutions' behavioral intention (BI) to employ AI robots in the classroom.

Davis 1989, established the technology acceptance model (TAM), which observes how users adopt and employ new technologies. Based on TAM model, the current study explores the Attitude

 

2.1 Theoretical Framework

This model proposes that the acceptance of AI among hospitality Educators is influenced by their awareness of AI technologies, their perceptions of these technologies, and various external and internal factors. To enhance the understanding of AI acceptance among faculty members, two external factors—“Facilitating Conditions” and “Perceived Compatibility” were included as external factors in the original TAM.

 

2.1.1 Technology Acceptance Model (TAM)

Davis 1989, validated the Technology Acceptance Model (TAM), which is a key framework for understanding technology acceptance. TAM suggests that two main factors influence technology acceptance, PU and PEOU. Perceived Usefulness (PU) is the degree to which an individual considers that applying a specific technology would progress their effectiveness at work. In the context of education, PU involves how AI tools are perceived to improve teaching effectiveness, learning outcomes, and administrative efficiency. For instance, AI applications like adaptive learning systems can provide personalized feedback and tailored learning experiences, which may be viewed as highly useful by educators (Davis D. , 1989). Perceived Ease of Use (PEOU) indicates the degree to which an individual has confidence in that using a technology would be free from any effort. This includes how simple it is to incorporate AI tools into current teaching methods and curriculum. Factors such as user-friendly interfaces, least training requirements, and smooth integration with current educational technologies impacts PEOU (Venkatesh V. &., 2008).

The best sign of an individual desire to use technology is perceived usefulness (PU), which is defined as the degree to which people think that utilizing a specific technology improves their performance (Davis, 1989) (Rafique, 2020; Al-Adwan, 2018; Sprenger, 2021). However, Perceived Ease of Use (PEOU), which measures how simple a technology is to use and favorably affects opinions of its usefulness, are closely related (Davis, 1989; Venkatesh & Davis, 2000). Particularly in education, where effortlessness of use and practicality encourage teachers to incorporate new technologies into teaching and learning, both PU and PEOU are important factors in determining the adoption of technology (Dhingra, 2019; Teo, 2011). According to research, PEOU has a substantial impact on PU and Behavioral Intention (BI) in educational settings (Chang, 2012; Rienties, 2016; Sánchez-Mena, 2017). The research has been demonstrated that ease of use and simplicity increase users' acceptance and pleasure of technology (Akdim, 2022; Davis, 1992; Wang, 2022). Behavioral Intentions BI, which in turn represents a person's strong intention to carry out a certain activity within their setting, is significantly shaped by Attitude (ATT), another essential component of the Technology Acceptance Model (TAM) put forward by Davis et al. (1989) (Fishbein, 1975).

According to TAM, greater acceptance and use of technology are correlated with better assessments of its utility and usability. In education sector, this means that Educators are more likely to adopt AI tools if they trust these tools will significantly progress their teaching and are easy to implement (Davis D. , 1989; Venkatesh V. &., 2008).

1. Hypothesis Development

By assuring that perceived ease of use directly decodes into the recognition and utilization of facilitating conditions, highlights the significance of user-centric design in AI development and encourages wider adoption and sustained engagement. Moreover, the successful integration of AI tools into learning processes is greatly influenced by the availability of support resources in education sector, making the positive relation between PEOU and facilitating conditions (Baig & Yadegaridehkordi, 2025). Similarly, a detailed hold of how PEOU affects enabling conditions can guide organizational AI implementation strategies, guaranteeing that the required support systems and infrastructure are in place to improve technology adoption and its use.

Facilitating Conditions (FC) refers to the extent to which individuals believe that gadgets and infrastructure are available for them to adopt a technology (Venkatesh, Morris, Davis, & Davis, 2003). Older persons may need more assistance than people in other age groups since they are not as accustomed to a new technology (Chen & Chan, 2013). When examining how FC affects older individuals' ICT acceptability, (Guner & Acarturk, 2020) found that FC has a beneficial impact on PEOU but not PU. Similarly, FC has a beneficial impact on PEOU, according to a study by (Li, Ma, Chan, & Man, 2019) on older individuals' adoption of smart wearables. FC significantly improves BI As these enabling conditions lower perceived barriers and boost self-efficacy, this hypothesis suggests that people's intention to use AI technologies increases significantly when they perceive adequate organizational and technical infrastructure, support, and resources (Almenara et al., 2025). This suggests that having the required tools, training, and support close at hand gives users confidence and increases their propensity to embrace AI (Kim et al., 2024). On the other hand, even when other elements, such as perceived usefulness, are high, a lack of perceived facilitating conditions can produce major obstacles and reduce behavioral intention (Sergeeva et al., 2025). This is consistent with research indicating that use behavior is strongly influenced by facilitating conditions (Rana et al., 2024). Evidence indicating that the existence of strong facilitating conditions can allay worries about implementation costs, especially for small and medium-sized businesses with little funding, supports this further (Soomro et al., 2025).

 

Hypothesis 1: Facilitating Conditions have significant positive effect on Behavioral Intentions

The degree to which a customer feels that utilizing a specific technology would enhance their ability to complete a given activity is known as perceived usefulness.  According to research, this variable directly and has a good effect on attitude (Lin, 2011). PC significantly improves BI This implies that users are more likely to develop a stronger intention to adopt and use AI technologies when they are seen as being in line with current work practices, values, and personal needs. This helps to bridge the gap between awareness and active engagement of the users (Cao et al., 2021). This perceived compatibility increases the viewpoint of sustained use by promoting a sense of natural alignment and lowering the reasoning effort needed for integration (Kelly et al., 2022). Perceived compatibility plays an important role in the adoption of technology, especially, when users are uncertain to break established routines (Liu & Ji, 2025).

 

Hypothesis 2: Perceived Compatibility have significant positive effect on Behavioral Intentions.

In academic environment it is important, where faculty and students' adoption of new AI tools can be greatly influenced by the perceived effort needed to learn and integrate them (Shata & Hartley, 2025). Consequently, an AI system that is easy to use is more likely to be acknowledged as being consistent with both individual learning preferences and current teaching methods, which encourages its widespread adoption. According to Lin et al. (2025), this alignment reduces cognitive load and facilitates a more seamless integration of AI tools into teaching strategies and student learning activities. The perceived usefulness and general acceptance of AI in academic settings can be greatly increased by such smooth integration, which is made possible by perceived ease of use (Biswas et al., 2025). PEOU significantly improves perceived compatibility. This implies that users are more likely to think that an AI system fits with their present demands, work styles, and values when they find it easy to use, which allows for a more seamless incorporation into their everyday routines. The ease of use in perceived compatibility, lowers resistance to integrating new AI technologies into daily tasks and increases the chances of sustained adoption (Mouloudj et al., 2025). Therefore, the impression that AI tools blend in perfectly with existing workflows and reasoning frameworks is greatly influenced by an intuitive user experience (Menon & Shilpa, 2023).

 

Hypothesis 3: Perceived Ease of Use have significant positive effect on Attitude.

This relationship is further supported by theories that suggest an individual's intention to adopt an innovation is significantly influenced by perceived benefits, which frequently include ease of integration (Jo, 2024). By creating a sense of natural fit within current operational frameworks, this suggests that designing AI tools with a strong emphasis on user-friendliness can proactively address potential adoption barriers (Ghimire & Edwards, 2024). PU significantly improves FC. According to this theory, users are more likely to believe that there are resources and favorable circumstances that support the use of an AI system when they believe it to be beneficial. This creates an atmosphere that is favorable for the system's adoption and ongoing use. This implies that the practical advantages of AI tools can increase understanding and respect for the conditions and auxiliary infrastructure that make their efficient use possible. A cycle of successful adoption and integration can thus be strengthened by a strong sense of usefulness, which can inspire people and organizations to actively look for or create facilitating conditions. Users may become more aware of or even support the resources (such as training and technical support) that further enable an AI tool's efficient operation, for example, if it dramatically increases productivity (Jonathan, 2025). On the other hand, if the value of an AI system is unclear, its enabling circumstances if they exist may be disregarded or seen as unimportant, which could result in underutilization. PU significantly improves PC This implies that people are more likely to see an AI system as compatible with their current tasks, values, and work practices when they are aware of its usefulness. This facilitates smooth integration and long-term engagement. Since it lessens the cognitive load needed to incorporate the AI tool into ingrained routines, this alignment between perceived usefulness and compatibility is essential for overcoming resistance to new technologies (Shrivastava, 2025). According to this convergence, if farmers find Ag 5.0 technology to be easy to use, beneficial, and supportive of improving their performance and current farming methods, they are likely to embrace it (Colavizza et al., 2020).

 

Hypothesis 4: Perceived Usefulness have significant positive effect on Attitude

ATT significantly improves FC. According to this theory, people who have a positive outlook on AI technologies are more likely to acknowledge and value the enabling circumstances that are required for their deployment and ongoing use (Ibrahim et al., 2025). Because of their optimistic outlook, people are more likely to recognize and take advantage of the resources, support networks, and environmental elements that facilitate the integration and functioning of AI systems (Ibrahim et al., 2025). This increased openness may result in a more proactive use of the support systems that are available, creating an atmosphere that is more favorable for the adoption of AI (Baharin, 2025).

Moreover, a positive outlook can inspire anyone to actively look for to create these enabling circumstances, which will support the effective implementation of AI (Shata & Hartley, 2025). This shows that a positive emotional and mental attitude toward AI will convert passive resource availability into active use, increasing the overall effectiveness of AI integration in a variety of contexts. As psychological factors frequently reconcile the practical application of technological resources, it is crucial to cultivate a positive attitude among users to ensure that facilitating conditions translate into effective AI adoption and sustained use (Ibrahim et al., 2025). ATT significantly improves PC, which suggests that users who have a favorable attitude toward AI technologies are more likely to believe that these tools are essentially aligned with their values, beliefs, and operational agendas (Valle et al., 2024). This alignment reduces cognitive dissonance and makes it easier to incorporate AI systems into current work procedures and individual beliefs (Na et al., 2022). Given that AI is viewed as a logical extension of existing practices rather than a disruptive innovation, this hypothesis implies that a positive attitude toward the technology promotes a sense of congruence, increasing the likelihood of adoption and long-term use (Chaveesuk et al., 2020).

 

Hypothesis 5: Atttitude has significant positive effect on Behavioral Intentions

BI significantly improves the actual use of AI According to this theory, a strong behavioral intention to use AI systems directly correlates with their actual use, acting as the direct precondition for the tangible implementation of AI technologies (Cabrera-Sánchez et al., 2020).

Hypothesis 6: Behavioral Intentions have significant positive effect on Actual usage of AI.

 

2.3 Key Variables

Variables	Definition	Source
Awareness of AI Technologies:	Knowledge and understanding of AI technologies and their potential applications in hospitality education.	(Davis, 1989).
Perceived Ease of Use (PEOU):	The extent to which a professor thinks that utilizing AI technologies would be effortless.	TAM (Davis, 1989).
Perceived Usefulness (PU):	The extent to which a professor thinks that using AI technologies would improve their teaching effectiveness or efficiency.	TAM (Davis, 1989).
Perceived Compatibility (PC):	The extent to which AI technologies are supposed to align with the instructors' wants, prior experiences, and present values.	DOI (Rogers, 2003).
Facilitating Conditions (FC):	Resources and support available to Educators for implementing AI technologies, including training and technical support.	TAM (Venkatesh et al., 2003).
Attitude Toward AI (ATT):	Overall positive or negative feelings that Educators have toward the use of AI in their teaching.	TPB (Ajzen, 1991).
Behavioral Intention to Use AI (BI):	The intention or willingness of Educators to integrate AI technologies into their teaching practices.	TAM (Davis, 1989).
Actual Usage of AI (AAI):	The extent to which AI technologies are actually used by Educators in their teaching activities.	TAM (Davis, 1989).

 

3. Research Methodology:

This study examines how Indian hospitality Educators perceive and embrace AI in hospitality education using a survey method and a descriptive research methodology. The target population for this study comprises hospitality educators (professors, lecturers and academicians) from 76 hospitality institutes of NCHMCT, and 50 institutes in Universities, and standalone institutions across India (considering a total of 150 hospitality institutes). Based on UGC norms for undergraduates programs, which recommend 1 educator per 20 students, and assuming an average intake of 120 students per institute, the total estimated population of hospitality educators in India is approximately 1000. Considering, Confidence level 95%, Margin of error 5%, population proportion 50%, using the formula for sample size calculation, the required sample size was determined to be 278 respondents. Educators/faculties of hospitality institutes from Delhi/NCR make up the target population. Respondents in this study were selected using convenience sampling, with a focus on academicians employed by hospitality institutions. Convenience sampling was chosen due to its practicality and suitability for quickly and conveniently drawing a sample from the target population. The Hospitality Institute's Educators were chosen as participants because of their expertise in the fields of hospitality management and education. It is recognized that convenience sampling is a widely used method for finding study participants and examining theoretical concepts and relationships (Winterstein, 2021).

Based on the literature review, the authors designed a systematic questionnaire for all selected constructs, drawing from well-designed and tested studies for data collection. A pilot study was conducted to evaluate the instrument's face and content validity. The questionnaire was then sent to experienced academicians, and their suggestions were incorporated. The final questionnaire consisted of 34 scale items covering various constructs such as Perceived Ease of Use (PEOU), Perceived Usefulness (PU), and Attitude towards AI (ATT), Facilitating Conditions (FC), Perceived Compatibility (PC), Behavioral Intention (BI), and Actual Use of AI (AAI), modified according to the study’s requirements. From "strongly agree" to "strongly disagree," a five-point Likert scale was used. The questionnaire was divided into three sections: the first section asked the participants' demographic profile, the second section focused on familiarity and awareness of AI among respondents, and the third section included 34 questions to measure respondents’ perceptions and acceptance of AI in the field of hospitality education.

The questionnaire was administered to 260 respondents using convenience sampling, and 245 responses were received. After excluding 15 partially filled responses, 245 valid samples were used for the study. 245 respondents who worked as Educators at hospitality institutions provided the data. The investigation was carried out from September to December of 2024.

 Four academicians with an average of 20-25 years of experience in the hospitality academic industry pre-reviewed the questionnaire to ensure its validity and accuracy in gathering reliable evaluation criteria.

The principal variables were modified from similar prior research (table 2.3). This study aimed to contribute to the extant literature concerning the perception and acceptance of artificial intelligence tools in hospitality education. The occurrence of AI tools within the educational realm has experienced significant growth and adaptability.

 

Data Analysis:

4.1 Descriptive Statistics

The collected data was analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). Structural Equation Modeling (SEM) was used to examine the relationships between the constructs, which were adapted from previous related studies. This study aims to contribute to the existing literature by exploring constructs such as awareness of Artificial Intelligence in hospitality education, Behavioral Intention (BI), Perceived Ease of Use (PEOU), Perceived Usefulness (PU), Facilitating Conditions (FC), Perceived Compatibility (PC), and Attitude towards Artificial Intelligence (ATT).

Table 4.1 describes the summary of demographic details of the respondents. Of 245 respondents, around 77.1% are male, while 22.9% are females.40.8 % of respondents were under 25-35 years of age, 55.1% were between 36-45 years of age, 4.1% were above 56 years of age. Data was examined further using demographic factors like educational qualifications, teaching experience, type of institution, and region of institute. 8.5 % respondents had post doctorate, 26.9% reported were doctorate, 61.2% had post graduate degree, 3.4% had graduation degree. 31% respondents from Government IHM, 24.9% were from Institute under university Set up, 44.1% were from Private Institute.

 

Table 4.1: Respondents Profile

Demographic Characteristics		Frequency	Percentage
Gender	Male	189	77.1
	Female	56	22.9
	Total	245	100.0
Age	25-35	100	40.8
	36-45	135	55.1
	56 and above	10	4.1
	Total	245	100.0
Educational Qualification	Post Doctorate	20	8.5
	Doctorate (Ph.D.)	66	26.9
	Post-Graduation	150	61.2
	Graduation	9	3.4
	Total	245	100.0
Teaching Experience	More than 15 Years	93	38.4
	11-15 Years	33	13.5
	5-10 Years	96	39.2
	Less Than 5 years	23	9
	Total	245	100.0
Type of Institution	Govt. IHM	76	31
	Institute under University Set-Up	61	24.9
	Private Institute	108	44.1
	Total	245	100.0
Region of Institute	Central India	35	14.3
	East India	14	5.7
	North India	169	68.9
	Northeast India	7	2.9
	West India	20	8.2
	Total	245	100.0

 

4.2 Awareness among Hospitality Faculty

The responses from hospitality faculty members about their knowledge, experience, application, and opinions of artificial intelligence (AI) in education are summarized in the table 4.2.

Participants were asked to rate their familiarity with AI as it relates to education. From the table it is observed that a 33.3% of the faculty is neutral or 22% were not very familiar with AI in education, emphasizing a need for increased awareness and training. This indicates that a significant portion of the faculty is either neutral or not very familiar with AI in education, highlighting a need for increased awareness and training. Extent to which they have attended AI related training or workshops, results show that while 25.1% faculty members have received basic or introductory training, 29.5% has not received any training, 20.6% is planning to attend in the future. The current usage of AI-based tools or applications in their institutions for educational purposes results suggests that 50.4% AI-based tools are somewhat used in institutions, 5.6% reporting wide usage, 14.2% no usage at all. The possible impact of AI on hospitality education results suggests that the 43.3% of the faculty perceives as positive, with a 21.3% remaining neutral, 11.3% somewhat negative, indicating diverse opinions on the subject. The results highlighted a mixed level of familiarity with AI among hospitality faculty, varying degrees of AI-related training, moderate usage of AI tools in educational environment, and generally positive perceptions of AI's potential impact on hospitality education. These findings recommended the need for more comprehensive training and awareness programs to enhance the integration of AI in hospitality education.

Table 4.2 Awareness among Hospitality Faculty
Awareness Among Hospitality Faculty		Frequency	Percentage
Rate your Familiarity with Artificial Intelligence (AI) as it relates to education.	Very Familiar	54	22.0
	Somewhat Familiar	21	8.5
	Neutral	82	33.3
	Not very familiar	54	22.0
	Not familiar at all	34	14.2
	Total	245	100.0
Indicate the extent to which AI- related training or workshops you have attended	Basic Training Received	62	25.1
	Extensive Training Received	7	2.8
	Introductory Training received	54	22.0
	No Training received	72	29.5
	Planning to Attend	50	20.6
	Total	245	100.0
Rate the current usage of AI- based tools or applications in the institution for educational purposes	Neutral	59	24.1
	Not sure	14	5.7
	Not used	35	14.2
	Somewhat Used	124	50.4
	Widely Used	13	5.6
	Total	245	100.0
How would you rate the potential impact of AI on hospitality education	Very Positive	106	43.3
	Somewhat Positive	59	24.1
	Neutral	52	21.3
	Somewhat Negative	28	11.3
	Very Negative	0	0.0
	Total	245	100.0

SMARTPLS 4 software was used to evaluate the data and examine the different variables are related to one another. A statistical method for investigative correlations between latent variables, partial least squares structural equation modeling (PLS-SEM) is especially helpful when the sample size is small (Hair, 2019).When conducting Partial Least Squares Structural Equation Modeling (PLS-SEM), selecting the appropriate sample size is crucial for ensuring the accurateness and validity of the findings. In PLS-SEM studies, the sample size is not fixed and depends on various factors, including the complexity of the model, the number of latent variables and indicators, the effect sizes, and the anticipated level of statistical control (Hair et al., 2013). Some researchers suggest that the sample size to indicator ratio should be at least 5:1 or 10:1, while others mentioned a minimum of 100–200 observations (Kock N. , 2018). For this study, a substantial sample size of 245 observations used for the analysis (Table 2.3). A comprehensive Collinearity assessment strategy is used to identify common method bias (CMB) in PLS-SEM (Hair et al., 2017; Kock, 2015). VIF values ought to be below the cutoff point of 3.3 (Hair J. H., 2017; Kock, 2015). This shows that there is no common technique bias in the model.

A Confirmatory Factor Analysis (CFA) was conducted to verify the reliability and validity of the measurement instrument. As shown in Table 3.3 factor loadings exceeding 0.70 to be considered acceptable, while loadings below 0.40 needs to be removed (Hair, 2014). Therefore, BI4, BI5, PC2, PU4 with factor loadings below 0.40, were excluded from the analysis. Additionally, Hair (2014) proposed a recommended threshold of 0.70 for Composite Reliability (CR) and 0.50 for Average Variance Extracted (AVE). In this study, all values exceeded these recommendations, indicating the acceptance of the measurement model and establishing convergent validity.

While assessing the reliability of multi-item scales, Cronbach's alpha, as introduced by Cronbach (1951), is a widely applied measure. In this study, Cronbach's alpha values for all constructs exceeded 0.7, indicating acceptable reliability for all measurement constructs.

The AVE squared is represented by the diagonal in the table, while the correlations between constructs are indicated by the values below the diagonal. Table 4.4 suggests that bold and diagonal values should be greater within their constructs compared to other constructs, both horizontally and vertically. Thus, discriminant validity is attained (Fornell & Larcker, 1981).

 

Table 4.3 Summary of Measurement Model

Construct			Loading	Alpha	CR	AVE
Perceived ease of Use (PEOU)	Indicate the extent of agreement with the following statements.  [AI can enhance the quality of education in the hospitality field.	PEOU 1	0.781	.864	.898	.708
	Indicate the extent of agreement with the following statements.  [AI-based tools can make teaching more efficient and effective.]-	PEOU 2	0.904
	Indicate the extent of agreement with the following statements.  [The use of AI in education can improve student engagement and learning outcomes.]-	PEOU 3	0.899
	Indicate the extent of agreement with the following statements.  [AI will reduce the need for traditional face-to-face teaching in hospitality education.]-	PEOU 4	0.773
Perceived Usefulness (PU)	Indicate the extent of agreement with the following statements.  [AI can provide personalized learning experiences for students.]-	PU1	0.862	.772	.826	.676
	Indicate the extent of agreement with the following statements.  [AI poses a threat to traditional teaching roles in hospitality education.]-	PU2	0.820
	Indicate the extent of agreement with the following statements.  [The ethical implications of AI in education need more attention.]-	PU3	0.784
Attitude (ATT)	How challenging do you foresee the integration of AI into hospitality education in terms of the following factors?  [Insufficient training for faculty]	ATT1	0.811	.810	.835	.636
	How challenging do you foresee the integration of AI into hospitality education in terms of the following factors?  [High costs associated with AI implementation]-	ATT2	0.769
	How challenging do you foresee the integration of AI into hospitality education in terms of the following factors?  [Resistance to change among faculty and students]-	ATT3	0.867
	How challenging do you foresee the integration of AI into hospitality education in terms of the following factors?  [Privacy and data security concerns]-	ATT4	0.736
Perceived Compatibility (PC)	Indicate the level of agreement with the following potential benefits AI could bring to hospitality education.  [Support for research and innovation in hospitality	PC1	0.904	.893	.895	.824
	How important are the following factors in encouraging the adoption of AI in teaching practices?  [Availability of training and support]-	PC3	0.913
	How important are the following factors in encouraging the adoption of AI in teaching practices?  [Proven improvements in student outcomes	PC4	0.907
Behavioral Intention (BI)	How important are the following factors in encouraging the adoption of AI in teaching practices?  [Institutional support and incentives	BI1	0.938	.949	.955	.868
	How important are the following factors in encouraging the adoption of AI in teaching practices?  [Peer recommendations and success stories]-	BI2	0.956
	Indicate the preferred areas for AI integration into the curriculum. [Classroom teaching and lectures]-	BI3	0.950
	How important is it to include AI as a part of the curriculum in hospitality education programs?	BI6	0.881
Actual use of AI (AAI)	Indicate the preferred areas for AI integration into the curriculum. Practical skill development (e.g., simulations)-	AAI1	0.835	.835	.846	.668
	Indicate the preferred areas for AI integration into the curriculum. [Student assessment and feedback]-	AAI2	0.844
	Indicate the preferred areas for AI integration into the curriculum. [Career counseling and placement]-	AAI3	0.817
	Indicate the preferred areas for AI integration into the curriculum. [Research and academic writing]-	AAI4	0.770
Facilitating Condition (FC)	Indicate the level of agreement with the following potential benefits AI could bring to hospitality education.  [Improved efficiency in teaching and learning]	FC1	0.855	.907	.911	.783
	Indicate the level of agreement with the following potential benefits AI could bring to hospitality education.  [Better personalization of learning experiences]-	FC2	0.850
	Indicate the level of agreement with the following potential benefits AI could bring to hospitality education.  [Enhanced data-driven decision-making	FC3	0.921
	Indicate the level of agreement with the following potential benefits AI could bring to hospitality education.  [Increased accessibility to quality education]-	FC4	0.911

 

Table 4.4 Fornell- Larcker Criterion

	AAI	ATT	BI	FC	PC	PEOU	PU
AAI	0.817
ATT	0.324	0.797
BI	0.718	0.526	0.932
FC	0.612	0.532	0.745	0.885
PC	0.728	0.574	0.902	0.743	0.908
PEOU	0.401	0.341	0.337	0.490	0.338	0.842
PU	0.447	0.332	0.338	0.396	0.292	0.849	0.822

 

The PLS-SEM method for hypothesis testing relies on bootstrapping standard errors to calculate the t-values of the path coefficients (Hair, 2014). Consequently, a bootstrapping procedure with 5,000 re-samples was performed. The results are summarized in Table 4.4 and illustrated in Figure 4.1. Through the measurement results in Table 4.5 below, conclusions can be drawn through the results calculation of the six hypotheses.

Behavioral intention is positively and significantly impacted by facilitating conditions. This implies that users are more likely to plan to utilize the system when they believe it has sufficient infrastructure and support such as technical assistance and resources. The path from Facilitating Conditions to Behavioral Intention is statistically significant, with a path coefficient of 0.170, a t-value of 5.582, and a p-value of 0.000, indicating a strong positive influence of facilitating conditions on users' intention to adopt the system. Moreover, perceived compatibility is the best indicator of behavioral intention. A high coefficient (0.783) shows that users' intention to embrace a system increases significantly which believes it fits well with the needs, values, and current behaviors. The path from Perceived Compatibility to Behavioral Intention is statistically significant, with a path coefficient of 0.783, a t-value of 27.191, and a p-value of 0.000.

Attitude toward the system is positively impacted by perceived ease of usage. A more positive opinion regarding the system is typically developed by users who finds it easy to use and navigate. The path from Perceived ease of Use to Attitude is statistically significant, with a path coefficient of 0.213, a t-value of 1.993, and a p-value of 0.000. User attitude is greatly improved by perceived usefulness. Users are more likely to have a favorable opinion of the system when they think it enhances their productivity or performance. The path from Perceived Usefulness to Attitude is statistically significant, with a path coefficient of 0.151, a t-value of 1.999, and a p-value of 0.000.

Behavioral intention is positively and significantly impacted by attitude. Although the effect size is small, a positive attitude toward the system increases the inclination to use it. The path from Attitude to Behavioral Intention is statistically significant, with a path coefficient of 0.114, a t-value of 2.423, and a p-value of 0.000. A reliable indicator of real adoption is behavioral intention. This demonstrates that there is a high probability that people who plan to utilize the system will really carry it out. The path from Behavioral Intention to Actual usage of AI is statistically significant, with a path coefficient of 0.718, a t-value of 18.119, and a p-value of 0.000.

According to the results, behavioral intention is significantly influenced by perceived compatibility, facilitating circumstances, and user attitudes that are influenced by usefulness and simplicity of use. In turn, actual adoption is highly predicted by behavioral intention. For system designers, educators, and legislators looking to improve user engagement and technology adoption, these results have useful implications.

 

Table 4.5 Path Analysis and Hypothesis Testing

Hypothesis		Original sample	Standard deviation	T statistics	P values	Hypothesis Result
H1	FC -> BI	0.170	0.030	5.582	0.000	Supported
H2	PC -> BI	0.783	0.029	27.191	0.000	Supported
H3	PEOU -> ATT	0.213	0.107	1.993	0.046	Supported
H4	PU -> ATT	0.151	0.105	1.999	0.000	Supported
H5	ATT -> BI	0.114	0.032	2.423	0.000	Supported
H6	BI -> AAI	0.718	0.040	18.119	0.000	Supported

 

Fig 4.1 Measurement Model

 

4. FINDINGS AND RESULTS:

Traditional teaching and learning paradigms are being redesigned by the revolutionary force that is artificial intelligence (AI) integration in education. AI is opening up new possibilities for Educators and students like by automating administrative responsibilities, personalizing learning experiences, and contributing data-driven insights. However, the adoption of AI in education also offers unique challenges, including concerns about equity, and data privacy, and the readiness of educational institutions to embrace these technologies. This study resulted in understanding the awareness of Artificial Intelligence in hospitality education, Behavioral Intention, Perceived Ease of Use, Perceived Usefulness, Facilitating Conditions, Perceived Compatibility, and Attitude towards Artificial Intelligence.

Results shows that 50.4% respondents are currently using AI and AI- based tools/applications for educational subjects. 43.3 % of respondents have shown quite positive response on potential impact of AI which it can mark in hospitality education. The perceived availability of resources and support (facilitating conditions) required for the usage of AI is greatly increased by a positive attitude toward its application. The belief that AI is compatible with Educators' current values, needs, and experiences is significantly influenced by their positive attitude toward the technology. Educators are more likely to actually deploy AI if they have a high behavioral intention to do so. Educators are more persuaded to utilize AI if they perceive technology as capable of fulfilling their needs. The perception of accessible resources and help is positively impacted when AI is seen as user-friendly. The opinion of AI's suitability for Educators' requirements and experiences is not greatly impacted by how simple it is to use. It's interesting to note that a negative but significant link suggests that users who believe AI to be effective may expect fewer resources to be required for support, which could lead to a minor decline in the sense of facilitating conditions. Educators' perceptions of AI's compatibility with their current systems and experiences are positively impacted by their opinion of its usefulness.

AI tools that are easy to use for curriculum preparation, adaptive learning, and student evaluation should be made available to educators. Building and maintaining a strong IT infrastructure is also critical, which includes compatible devices and fast internet. Train Educators about AI tools and their uses by conducting practical training sessions like workshops and lectures. Furthermore, provide continuing education on incorporating AI into teaching with an emphasis on particular applications such as administrative automation, grading, or personalized learning. Create specialized IT support teams to help Educators who are experiencing technical difficulties when utilizing AI products. Additionally, incentivize Educators that successfully use AI into their lesson plans to promote wider adoption. Create precise AI adoption standards that specify how AI should be used in schools to guarantee moral and efficient use. Develop more faculty development programs, refresher, and training modules to improve compatibility and inspire behavioral goals. Facilitate authentic, experiential learning for students by developing AI-driven tools like virtual laboratories and simulations that replicate real hospitality culture, including areas of hotel management, food and beverage services, and customer interactions. Cloud-based training environments and AI-enhanced curriculum design may not be perfectly compatible with perceived ease of use, despite their simplicity, underscoring the need for additional arrangement. AI can increase efficiency of educators and administration in resource allocation, including project assignment, guest speaker scheduling, and classroom management. To improve perceived utility and compatibility, include adaptive learning platforms which modify course content as per the student's progress, preferences, and learning speed.

 

5.2 Practical Implications

The study's conclusions provide a number of practical recommendations for academic institutions looking to encourage academics and instructors to successfully use AI tools. First, it's critical to foster a positive attitude toward AI. Through student driven presentations, success stories, and candid discussion, educational institutions can promote a culture that emphasizes AI's potential to improve teaching methods. Highlighting AI's perceived usefulness (PU) by demonstrating how it improves student results and instructional effectiveness can have a big impact on educators' readiness to implement these technologies. Ensuring perceived ease of use (PEOU), which reduces obstacles to start and promotes experimentation, can be achieved by user-friendly design, practical training, and easily accessible support.

AI tools must be compatible with current teaching methods and pedagogical objectives in order to be used as widely as possible. Educators should consider AI as a supplement rather than a disruption due to its customizable features and compatibility with curriculum objectives. Furthermore, successful integration depends on enabling conditions including reliable infrastructure, device access, and committed technical support. To retain academics knowledgeable and comfortable with AI, institutions should make investments in ongoing professional development by providing frequent training and updates.

Lastly, encouraging behavioral intention (BI) demands a comprehensive strategy that incorporates institutional support, positive experiences, and obvious advantages. Educators are more likely to use AI technologies consistently when they believe them to be both convenient and helpful. These real-world applications highlight how crucial it is to have a supportive ecosystem that not only introduces AI but also maintains its beneficial application in learning environments.

Higher education institutions (HEIs) must start offering courses to staff and students in order to solve moral conundrums and find a balance between creativity and moral obligation. To gradually increase AI proficiency across fields, a formal structure is needed. The size of the AI shift must be acknowledged by HEIs, and resources must be set aside to get faculty ready for integration, including updating teaching, evaluation, and learning methods. The successful integration of AI into higher education is still developing as it’s still a relatively new technology. For educators to use AI tools in a meaningful way, they require independence and specialized training. Faculty development is becoming more and more important, with an emphasis on two main areas: pedagogical application and AI literacy. More efficient utilization and information acquisition are made possible by improving instructors' comprehension of generative AI (Kong, 2024). Development programs are to offer different examples that are in line with educational objectives, as well as theoretical underpinnings and practical abilities. It is crucial to take a student-centered approach that prioritizes logical thinking, active learning, and practical experience (Salinas-Navarro, 2024). In order to facilitate faculty adaptation and guarantee the long-term integration of AI technologies in higher education, ongoing professional development will be provided through workshops, seminars, and online courses on AI principles and instructional techniques.

 

5.3 CONCLUSIONS:

AI in education have the power to revolutionize teaching methods and enhance student learning. An overview of AI's present and future roles in education is given in this paper, with a focus on the necessity of addressing obstacles and seizing opportunities within the Indian educational system. By building infrastructure, training faculty more effectively, and upholding moral principles, Indian Educational Institutions can promote inclusive and fair learning environments. However, the commercialization of education through AI-driven technologies raises both practical and ideological concerns which should be thoroughly examined. This research aims to systematically analyze the benefits and drawbacks of integrating AI into inclusive education in India. Through the use of innovative tools and techniques, AI has the potential to significantly improve learning outcomes, increase student engagement, and increase teaching effectiveness in the hospitality sector.

Hospitality Institutes should provide strong IT infrastructure and easily controllable AI technologies to successfully incorporate AI into hospitality education. To increase teaching effectiveness and student engagement, provide educators with hands-on training and continual professional development, set up professional IT support teams, encourage the adoption of AI, provide clear guidelines, produce creative learning opportunities, and allocate resources as efficiently as possible. These implications will simplify the adoption of AI in hospitality education by the education sector, resulting in increased teaching effectiveness, better student engagement, and optimum learning results all of which will contribute to the growth of a more creative and productive learning environment.

 

1. Limitations and Future Research

This study was limited by the local focus of Delhi/NCR and despite the expansion of hospitality education, the breadth of the study remains narrow. The long-term impact of AI on education requires more research. In addition, there is a lack of empirical research on the specific challenges faced by educators in adopting AI. In future researchers should focus on study the long-term impact of AI on curriculum alignment and student outcomes, evaluate the role of AI in linking the gaps between academic learning and industry needs.

 

 

References

Airey, D. T. (2015). The managerial gaze: The long tail of tourism education and research. . Journal of Travel Research, 54(2), 139–151. https://doi.org/10.1177/0047287514522877.

Ajzen, I. (1991). The Theory of planned behavior. . Organizational Behavior and Human Decision Processes.

Akdim, K. C. (2022). The role of utilitarian and hedonic aspects in the continuance intention to use social mobile apps. . Journal of Retailing and Consumer Services, 66, Article 102888. https://doi.org/10.1016/j.jretconser.2021.1.

Al-Adwan, A. S.-A. (2018). Solving the mystery of mobile learning adoption in higher education. . International Journal of Mobile Communications, 16(1), 24-49. https://doi.org/10.1504/IJMC.2018.088271.

Aydin, B. &. (2024). Navigating the Frontier: Addressing Artificial Intelligence Challenges in Tourism and Hospitality Education. Transnational Education Review, 2(1), 49-53.

Cano, J. R. (2024). Unlocking innovation: how enjoyment drives GenAI use in higher education. In Frontiers in Education (Vol. 9, p. 1483853). Frontiers Media SA.

Chai, J. L. (2020). Factors infuencing students’ behavioral intention to continue artifcial intelligence learning. In International Symposium on Educational Technology (ISET) 147–150 (IEEE, 2020).

Chang, C.-C. Y.-F.-S. (2012). Perceived convenience in an extended technology acceptance model: Mobile technology and English learning for college students. . Australasian Journal of Educational Technology, 28(5). https://doi.org/10.14742.

Chen, K., & Chan, A. (2013). Use or non-use of gerontechnology—A qualitative study. Int. J. . Environ. Res. Public Health 2013, 10,.

Chen, X. X. (2020). Application and theory gaps during the rise of artifcial intelligence in education. . Comput. Educ. Artif. Intell. 1, 100002 .

Cheng, E. (2019). Choosing between the theory of planned behaviour (TPB) and the technology acceptance model (TAM). . Educational Technology Research and Development, 67(1), 21–37.

Cronbach, L. J. (1951). Coefficient alpha and the internal structure of tests. . Psychometrika, 16(3), 297-334. https://doi.org/10.1007/BF02310555.

Daniel, A. C. (2017). Tourism education: What about entrepreneurial skills? . Journal of Hopsitlaity and Tourism Management, 30, 65–72. https://doi.org/10.1016/j.jhtm.2017.01.002.

Davis, D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. . MIS quarterly, 319-340.

Davis, F. D. (1992). Extrinsic and Intrinsic Motivation to Use Computers in the Workplace1. . Journal of Applied Social Psychology, 22(14), 1111–1132. https://doi.org/10.1111/j.1559-1816.1992.tb00945.x.

Davis, N. G. (2019). Online teaching: Advantages, obstacles and tools for getting it right. . Journal of Teaching in Travel and Tourism, 19(3), 256–263. https://doi.org/10.1080/15313220.2019.1612313.

Deri, M. Z. (2024). Digital Future of the Hospitality Industry and Hospitality Education Globally. In: Sharma, A. (eds) International Handbook of Skill, Education, Learning, and Research Development in Tourism and Hospitality. Springer International Handbooks of Education. Springer, Singapore. https://doi.org/10.1007/978-981-99-3895-7_14-1.

Dhingra, M. &. (2019). Applications of Perceived Usefulness and Perceived Ease of Use: A Review. . 2019 8th International Conference System Modeling and Advancement in Research Trends (SMART), 293–298. https://doi.org/10.1109/SMART46866.2019.9.

E Goh, M. S. (2020). Integrating Information & Communication Technologies (ICT) into classroom instruction: teaching tips for hospitality educators from a diffusion of innovation approach.

Fishbein, M. &. (1975). Belief, attitude, intention and behaviour: An introduction to theory and research. . Reading: Addison-Wesley Publication Company, 1975.

Fornell, C. &. (1981). Evaluating structural equation models with unobservable variables and measurement error. . Journal of Marketing Research, 18(1), 39-50. https://doi.org/10.2307/3151312.

Fotiadis, A. &. (2019). Developing a framework for designing an Events Management Training Simulation (EMTS). . Journal of Hospitality, Leisure, Sport & Tourism Education, 16, 59–71.

Goh, E. &. (2020). Applying the technology acceptance model to understand hospitality management students’ intentions to use electronic discussion boards as a learning tool. . Journal of Teaching in Travel & Tourism, 21(2), 142–154. https://doi.org/10.1080/15313220.2020.1768621.

Goh, E. &. (2020). Four decades (1980-2020) of hospitality and tourism higher education in Australia: Developments and future prospects. Journal of Hospitality & Tourism Education, 32(4), 266-272.

Goh, E. &. (2020). Integrating information and communication technologies (ICT) into classroom instruction: Teaching tips. . Journal of Teaching in Travel and Tourism, 20(2), 156–165.https://doi.org/10.1080/15313220.2020.1740636.

Goh, E. N. (2017). Marketing private hotel management schools in Australia. Asia Pacific Journal of Marketing and Logistics, 29(4), 880–889. https://doi.org/10.1108/APJML-09-2016-.

Groebner, D. F. (2005). Business statistics: A decision making approach. . Pearson Education.

Guner, H., & Acarturk, C. (2020). The use and acceptance of ICT by senior citizens: A comparison of technology acceptance model (TAM) for elderly and young adults. . Univers. Access Inf. Soc. 2020, 19, 311–330.

Hair et al. (2013). Partial least squares structural equation modeling: Rigorous applications,better results and higher acceptance. Long Range Planning, 46, 1–12. https://doi.org/10.1016/j.lrp.2013.01.001.

Hair et al. (2017). A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM) (2nd ed.). . Sage Publications.

Hair, J. F. (2019). When to use and how to report the results of PLS-SEM. . European business review, 31(1), 2-24.

Hair, J. H. (2017). A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM) (2nd ed.). . Sage Publications Inc., Thousand Oaks, CA.

Hair, J. J. (2014). Partial least squares structural equation modeling (PLS-SEM): An emerging tool in business research. . European Business Review, 26(2), 106-121. https://doi.org/10.1108/EBR-10-2013-0128.

Hew, K. F. (2010). Student contribution in asynchronous online discussion: A review of the research and empirical exploration. . Instructional science, 38, 571-606.

Holmes, W. B. (2023). Artifcial Intelligence in Education. (Globethics Publications.

Huang, C. E. (2020). Discovering the creative processes of students: Multi-way interactions among knowledge acquisition, sharing and learning environment. . Journal of Hospitality, Leisure, Sport & Tourism Education, 26, 100237.

Huang, Y. L. (2014). Jigsaw-based cooperative learning approach to improve learning outcomes for mobile situated learning. . Journal of Educational Technology and Society, 17(1), 128–140. https://psycnet.apa.org/record/2014-06607-012.

KF Hew, W. C. (2010). Student contribution in asynchronous online discussion: A review of the research and empirical exploration. Springer.

Knight, D. W. (2020). Launching an online graduate degree for tourism management in China: lessons in Chinese-foreign cooperation. . Journal of Teaching in Travel & Tourism, 20(4), 326-346.

Kock. (2015). Common method bias in PLS-SEM: A full collinearity assessment approach. International Journal of e-Collaboration, 11(4), 1-10. https://doi.org/10.4018/ijec.2015100101.

Kock, N. (2018). Minimum sample size estimation in PLS-SEM: An application in tourism and hospitality research. InApplying partial least squares in tourism and hospitality research (pp. 1–16). . Emerald Publishing Limited. https://doi.org/10.1108/978-1-78756-699-620181001.

Kong, S. C. (2024). Examining teachers’ behavioural intention of using generative artificial intelligence tools for teaching and learning based on the extended technology acceptance model. . Computers and Education: Artificial Intelligence, 7, 100328.

Kong, S. C.-Y. (2024). Developing an artificial intelligence literacy framework: Evaluation of a literacy course for senior secondary students using a project-based learning approach. . Computers and Education: Artificial Intelligence. , Article 100214. https://doi.org/10.1016/j.caeai.2024.100214.

Lee, P. C. (2016). Educational technology in hospitality management programs: Adoption and expectations. . Journal of Teaching in Travel & Tourism, 16(2), 116-142.

Li, J., Ma, Q., Chan, A., & Man, S. (2019). Health monitoring through wearable technologies for older adults: Smart wearables acceptance model. . Appl. Ergon. 2019, 75, 162–169.

Li, W. Z. (2024). An explanatory study of factors influencing engagement in AI education at the K-12 Level: an extension of the classic TAM model. . Scientific Reports, 14(1), 13922.

Lin, F. F. (2011). Assessing citizen adoption of e-Government initiatives in Gambia: A validation of the technology acceptance model in information systems success. Government Information Quarterly, 28(2), 271–279, 2011. https://doi.org.

Liu, Q. G. (2020). Understanding academics‘ adoption of learning technologies: A systematic review. Computers & Education, 151, 103857. https://doi.org/10.1016/j.

McCarthy, J. (2012). International design collaboration and mentoring for tertiary students through Facebook. . Australasian Journal of Educational Technology, 28(5).

Miller, J. P. (2012). Determining the attitudes of students toward the use of a classroom response in hospitality courses. Journal of Hospitality & Tourism Education, 24(2-3), 73-79.

Mulaudzi, L. &. (2024). Student perspectives on optimising AI tools to enhance personalised learning in higher education. . Interdisciplinary Journal of Education Research, 6(s1), 1-15.

O Zawacki-Richter, V. M. (2019). Systematic review of research on artificial intelligence applications in higher education–where are the educators? Springer.

Q Liu, S. G. (2020). Understanding academics' adoption of learning technologies: A systematic review. Elsevier.

Rafique, H. A. (2020). Investigating the Acceptance of Mobile Library Applications with an Extended Technology Acceptance Model (TAM). . Computers & Education, 145, Article 103732. https://doi.org/10.10.

Rienties, B. G.-B. (2016). Why some teachers easily learn to use a new virtual learning environment: a technology acceptance perspective. Interactive Learning Environments, 24(3), 539–552. https://doi.org/10.1080/10494820.2014.881394.

Rogers, E. (2003). Diffusion of innovations . (5th ed.). New York: Free Press.

Salinas-Navarro, D. E.-P.-V. (2024). Using generative artificial intelligence tools to explain and enhance experiential learning for authentic assessment. . Education Sciences, 14(1), 83. https://doi.org/10.3390/educsci14010083.

Sánchez-Mena, A. M.-P.-M. (2017). The Effect of Age on Teachers’ Intention to Use Educational Video Games: A TAM Approach. . The Electronic Journal of E-Learning, 15, 355. https://eric.ed.gov/?id=EJ1154704.

Sharma, S. &. (2024). Impact of AI-Based Chatbots on Faculty Performance in Higher Education Institutions. In Innovation in the University 4.0 System based on Smart Technologies (pp. 83-100). . Chapman and Hall/CRC.

Sharma, S. a. (2023). Hospitality education in Asia: is it sufficiently equipped to prepare future hospitality leaders? Worldwide Hospitality and Tourism Themes, Vol. 15 No. 3, pp. 207-211. https://doi.org/10.1108/WHATT-02-2023-0026.

Shishakly, R. A. (2024). The infuence of using smart technologies for sustainable development in higher education institutions. Int. J. . Data Netw. Sci. 8(1), 77–90 .

Sprenger, D. A. (2021). Technology acceptance of four digital learning technologies (classroom response system, classroom chat, e-lectures, and mobile virtual reality) after three months’ usage. International Journal of Educational Technology in Higher Education, 18(1), 8. https://doi.org/10.1186/s41239-021-00243-4.

Teo, T. (. (2011). Modeling the determinants of pre‐service teachers’ perceived usefulness of e‐learning. . Campus-Wide Information Systems, 28(2), 124–140. https://doi.org/10.1108/10650741111117824.

Terblanche, N. M. (2023). Performance matters, students’ perceptions of artifcial intelligence coach adoption factors. Coach. Int. J. Teor. 16, 100–114 (2023).

Venkatesh, V. &. (2000). A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies. . Management Science, 46(2), 186–204. https://doi.org/10.1287/mnsc.46.2.186.11926.

Venkatesh, V. &. (2008). Technology acceptance model 3 and a research agenda on interventions. . Decision sciences, 39(2), 273-315.

Venkatesh, V., Morris, M., Davis, G., & Davis, F. (2003). User acceptance of information technology: Toward a unified view. . MIS Q. 2003, 27, 425–478.

Wang, Y.-M. C.-C.-H.-S.-Y.-F. (2022). Determinants of students’ adoption of virtual reality-based learning systems: An individual difference perspective. . Innovations in Education and Teaching International, 1–11. https://doi.org/10.1080/14703297.2022.2098512.

Winterstein, J. &. (2021). "Is local the new organic? Empirical evidence from German regions. British Food Journal, 123(11), 3486-3501. https://doi.org/10.1108/BFJ-06-2020-0517.

Zawacki-Richter, O. M. (2019). Systematic review of research on artificial intelligence applications in higher education–where are the educators? International Journal of Educational Technology in Higher Education, 16(1), 1-27.

Zhang, K. L. (2024). How can ChatGPT assist hospitality and tourism education? . Journal of Teaching in Travel & Tourism, 24(4), 438-448.