Abstract
Purpose
This study aimed to identify factors influencing AI/chatbot usage in education and research, and to evaluate the extent of the impact of these factors.
Design/methodology/approach
This study used a mixed approach of qualitative and quantitative methods. It is based on both primary and secondary data. The primary data were collected through an online survey. In total, 177 responses from teachers were included in this study. The collected data were analyzed using a statistical package for the social sciences.
Findings
The study revealed that the significant factors influencing the perception of the academic and research community toward the adoption of AI/interactive tools, such as Chatbots/ChatGpt for education and research, are challenges, benefits, awareness, opportunities, risks, sustainability and ethical considerations.
Practical implications
This study highlighted the importance of resolving challenges and enhancing awareness and benefits while carefully mitigating risks and ethical concerns in the integration of technology within the educational and research environment. These insights can assist policymakers in making decisions and developing strategies for the efficient adoption of AI/interactive tools in academia and research to enhance the overall quality of learning experiences.
Originality/value
The present study adds value to the existing literature on AI/interactive tool adoption in academia and research by offering a quantitative analysis of the factors impacting teachers' perception of the usage of such tools. Furthermore, it also indirectly helps achieve various UNSDGs, such as 4, 9, 10 and 17.
Keywords
Citation
N., A., Devesh, S., M.L., A., Suraj, N., Acharya, P. and M.S., D. (2024), "Navigating AI and chatbot applications in education and research: a holistic approach", Quality Education for All, Vol. 1 No. 1, pp. 277-300. https://doi.org/10.1108/QEA-10-2023-0005
Publisher
:Emerald Publishing Limited
Copyright © 2024, Abhishek N., Sonal Devesh, Ashoka M.L., Neethu Suraj, Parameshwara Acharya and Divyashree M.S..
License
Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial & non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode
1. Introduction
In the technology-based environment and ever-changing landscape of education and research, the inculcation of artificial intelligence (AI) and conversational tools such as ChatGpt/Chatbots has become increasingly prominent (Eguchi et al., 2021). These tools offer many potential benefits, ranging from revolutionizing teaching and learning experiences to advancing and streamlining research processes. However, their employment is not free from complexities, challenges and ethical considerations (Baidoo-Anu and Ansah, 2023). The potential benefits of adopting these tools are a key factor in encouraging academics and researchers to improve their efficiency in their roles (Abbas et al., 2023). Furthermore, these tools also assist them in performing their roles in confirmation of real-world practices (Owan et al., 2023). In addition, it also helps to enhance their abilities with increased research efficiency and analyzes a wider range of data sets for value-added outcomes. These tools have also motivated educational institutions to reap the benefits of AI solutions (Chounta et al., 2022; Rahman and Watanobe, 2023). In another context, these tools offer opportunities for academic and research staff to create and implement innovative teaching methods, support collaborative research and automate routine administrative functions. This, in turn, produces more time spent on meaningful work (Grassini, 2023). However, many academic and research staff members are not fully aware of the capabilities, benefits and limitations of these tools. Therefore, institute initiatives are necessary to increase awareness to implement AI-based tools to enhance academic and research efficiency (Lindner et al., 2019; Chounta et al., 2022; Lee and Perret, 2022). Further, it is also necessary to ensure the long-term sustainability of these technological initiatives in academic and research environments to gain the trust of the various stakeholders involved (Gupta et al., 2020; Abad-Segura et al., 2020). Aspects of scalability, adaptability, maintenance and monitoring of these tools over time are an unanswered question (Sok and Heng, 2023).
There are some challenges in initiating AI in academia and research, including technical challenges in the implementation of such tools, additional financial burdens and potential resistance to change among academic and research staff. Resolving these issues requires more time, effort and support from staff to bridge the gap during the transition (Opara et al., 2023; Shidiq, 2023). The employment of AI-based tools in academics and research poses some risks that may be associated with data privacy, security breaches, algorithmic biasedness and replacing teachers and researchers (Peres et al., 2023; Rahman and Watanobe, 2023). Most importantly, there are many ethical considerations behind the adoption of AI-based tools in education and research, such as transparency, fairness, accountability and the responsible use of such tools without violating legal and ethical norms (Halaweh, 2023). Analyzing and evaluating these factors before the implementation of AI tools in education and research is essential. Therefore, the present study helps various stakeholders and regulatory authorities frame policies and implement AI tools for education and research. This study analyzed various factors influencing the adoption of AI/interactive tools in education and research. The remainder of this paper is organized as a literature review, theoretical framework, hypotheses development, methodology, results, discussion and conclusion.
2. Background literature review
As the concept of the study is emerging and needs of the hour, it is essential to analyze the research trend to identify the specific gap to be filled by the current study. This section discusses trends in the literature on the current topic.
AI/chatbots offer many opportunities to the stakeholders of educational institutions, such as Teachers, Researchers, Administrators and others. However, the application of such technologies in education and research remains limited (Yang and Evans, 2019). In addition, Zheng et al. (2022) also highlighted the opportunities of these technologies in terms of personalized learning experience, 24/7 learning support, enhanced learning experience, expanded diversity in learning opportunities and career counseling and guidance. On the other hand, they also highlighted the challenges of such technologies in terms of local language processing, low speed of response, accuracy and reliability, interaction and customization and information security.
The use of AI/chatbot technologies offers a platform for interactive teaching, learning and research, which could be a major influencing factor in the application of such technologies for education and research (Sandu and Gide, 2019). Furthermore, these technologies help establish a worldwide trustworthy and sustainable ecosystem of knowledge, skills and values by collating all stakeholders in a single platform (Sandu and Gide, 2019; Karyotaki et al., 2022; Rossettini et al., 2023). On the other hand, Wang et al. (2023) noted that AI/Chatbots certainly ease the teaching-learning process and also enhance the efficiency of the educational administrative process. These technologies are essential and beneficial for designing and offering a blended educational model (Ilieva et al., 2023). Furthermore, AI/chatbots reduce many technical tasks, such as the translation of learning content from one language to another, more specifically, in the class border learning environment (Han, 2020; Aleedy et al., 2022). Furthermore, these technologies also assist and help to smooth the learning of different cultures and languages with minimal effort and resources. Hence, the speed and efficiency of learners will improve significantly (BOZDOĞAN and EKMEKÇİ, 2023; Zhai and Wibowo, 2022).
The use of AI/chatbots in education and research requires a change in the academic rules and evaluation procedures used in educational institutions (Gill et al., 2024; Al-Emran et al., 2023) highlighted that the performance expectancy, effort expectancy, habits of learners and perceived threats are the most influential factors for the use of AI/Chatbots in education and research. Kelly et al. (2022) pointed out that not all applications of AI/chatbots can be generalized to account for the influence of their adoption in a particular field. Technological proficiency in teachers and learners is a significant factor that influences the adoption of AI/chatbots in education and research (Min et al., 2021; Zhang et al., 2023). Technology-based education enhances learners’ learning experiences and potentiality (Gallina, 2023). More importantly, the functional aspects of any technology, perceived ease of use and social influence factors significantly affect its adoption, as in the case of AI/chatbots (Malik et al., 2021; Nicolescu and Tudorache, 2022; Bilquise et al., 2023). However, ethical issues involved in the use of AI/chatbots in education and research are also a prominent aspect to be considered before and after their adoption (Mvondo et al., 2023).
2.1 Artificial intelligence tools in teaching, learning and research
The integration of technology into education has gained prominence because of the COVID-19 outbreak (Fergus et al., 2023). The transformation of education is directly linked to the application of technologies, such as artificial intelligence. These technologies have opened up new possibilities and pitfalls for teaching and research (Popenici and Kerr, 2017). A recent advancement in AI applications is the newly invented assistive tool ChatGPT, which is a chatbot with extraordinary human abilities. In a study by Susnjak (2022), ChatGPT was shown to be capable of exhibiting critical thinking skills and generating highly realistic texts with minimal input. Further, there are concerns about cheating during online sessions that need to be prioritized by taking proper measures to maintain ethics and integrity in education. In another study by Sullivan et al. (2023), assistive tools such as ChatGPT can positively help students in numerous ways by enhancing the academic success of students from different equity groups, helping students with disabilities, reducing stigma around seeking help and helping students translate content from one language to another. Qadir (2022) also highlighted that these technologies help enhance the learning experiences of learners by supporting them in obtaining customized feedback, explanations and assisting in developing realistic virtual simulations for hands-on learning. Further, the researcher also highlighted that negative aspects, such as the use of generative AI in education, raise ethical concerns, such as the potential for unethical or dishonest use by students and the potential unemployment of humans who are made redundant by technology. Furthermore, it is suggested that students take advantage of the benefits offered by Generative AI technologies while avoiding negative consequences.
Borenstein and Howard (2021) in their study pointed out that AI-based technologies are becoming pervasive and reshaping the world unimaginably, which though beneficial can also sometimes be harmful. Cotton et al. (2023) noted that Chatbot/Chat GPTs, ChatGPT offer many benefits such as increased student engagement, collaboration and accessibility, it also raise concerns about risks to academic integrity and ethics. A similar concern was raised by many authors; for instance, (Sullivan et al. (2023) and Mijwil et al. (2023) argued that the application of AI in academic research raises concerns about ethics and integrity. Further, they stressed that there is a lack of technologies that can detect such violations, which creates a significant challenge for academic writing. Furthermore, Peres et al. (2023) and King and ChatGPT (2023) in their study they opined that students can use ChatGPT to cheat in writing assignments, which can have serious consequences, such as failing grades and academic integrity. In addition, they stressed that it would negatively impact their critical thinking abilities. Cribben and Zeinali (2023) also expressed concern that greater reliance on AI/Conversational tools may also affect critical thinking and problem-solving skills among learners and educators.
Chen et al. (2020), AlAfnan et al. (2023) in their study, it is highlighted that ChatGPT has the potential to replace the existing search engines as it is capable of producing accurate and reliable ideas to answer descriptive and application questions. Furthermore, it is also noted that ChatGPT provides an effective platform for educational institutions to set up technology-integrated classrooms, organize workshops, have discussions and evaluate generated responses. They also pointed out that these technologies can be beneficial in designing courses, creating content, grading and assessments to evaluate students’ performance.
In contrast, Farrokhnia et al. (2023) in their study it is noted that ChatGPT can threaten the education system by causing a lack of understanding of the context, threatening academic integrity, causing discrimination to continue in education, democratizing plagiarism and declining high-order cognitive skills. In addition, it is also pointed out that due to concerns related to academic ethics and integrity, many publishers do not consider the work generated by generative AI as the work of submitting authors and demand appropriate clarifications, where necessary.
2.2 Artificial intelligence/chatbots and academic research writing
Artificial Intelligence tools are increasingly being used by researchers for writing research reports and analyses, and ChatGPT has recently been cited as a co-author in some research articles (Burger et al., 2023; Somasundaram, 2023) observed that technological tools, such as ChatGPT, can serve as powerful tools to expedite the process of writing and publishing research reports. However, the author also stressed issues of academic integrity and ethics. However, it is equally important to note that not all publishers allow ChatGPT to be listed as a co-author and many publishers are prohibited from listing ChatGPT as a co-author (Peres et al., 2023).
Mijwil et al. (2023) pointed out that the users of AI tools must be careful about ethical considerations, transparency and accountability of their contents. In support of this, Lund and Wang (2023) opined that AI tools pose serious concerns about the unethical use of technology in academia and research; therefore, monitoring this issue is a big challenge. Academic institutions are responsible for framing strategies to monitor such issues to ensure ethics and integrity. Crust (2023) also highlighted the potential loss of demand for skilled labor because of ChatGPT. Most studies have thus noted the implication of ChatGPT and other AI tools for jobs, specifically in academic and research environments.
2.3 Prevention of dishonest use of artificial intelligence tools in academics and research
AI interactive tools can prove to be useful for students and educators in numerous ways and are subject to various ethical considerations. Educational institutions and publishers must use many strategies and serious measures to mitigate the risk of academic dishonesty (Halaweh, 2023). Cotton et al. (2023) have recommended that educational institutions adopt strategies, policies and procedures to design, train and support and other ways to detect and prevent misuse and cheating in the learning process.
AlAfnan et al. (2023) in their study noted that AI tools skilfully paraphrase the regenerated responses in a way that cannot be detected by similarity detection tools. They also opined that similarity detection software providers must upgrade their software to avoid such incidents from slipping. To overcome the dishonest use of AI tools by learners, King and ChatGPT (2023) suggested that universities and educational institutions should implement alternative methods such as oral presentations, group projects and hands-on activities that involve more interaction and engagement of students. Borenstein and Howard (2021) suggested that there is a need for support from the government and other research institutions to resolve these issues. Sullivan et al. (2023) opined that to eliminate cheating and misuse of AI tools by teachers’ researchers and academicians, there is a need to rethink and redesign the assignments in ways that can limit the capabilities of AI tools.
2.4 Artificial intelligence/conversational tools versus changing role of academic staff
AI/Conversational tools can also be expected to have implications for teachers. This is due to the growing application and use of AI tools in education. Studies by many authors, such as Qadir (2022), Sullivan et al. (2023) and Cotton et al. (2023), suggested that interactive tools effectively help learners understand complex concepts with minimal time and effort. It also assists students in writing their academic assignments, projects and materials. However, this can pose a potential threat or raise questions regarding the need for teachers in the future or partially reduce the number of teachers in an academic environment. Furthermore, it also creates difficulties in the assessment and evaluation process. Selwyn (2019) noted that, although most teachers are confident about the unlikeliness of being pushed aside by AI/Chatbots anytime soon, the extent to which human teachers might be displaced by robots in the near future is worth exploring and cannot be neglected.
Conversely, Popenici and Kerr (2017) argued that AI is not ready to replace teachers; however, it can aid in modifying the services and nature of teachers’ functions. Celik et al. (2022) noted that the evolution of AI-based digital education does not imply that universities are education institutes will need fewer teachers in the future. Bosede and Cheok (2018) in their study noted the opinion of educational stakeholders, stating that AI/robots are neither about nor capable of occupying the role of teachers. However, the authors also noted the many advantages of robot teachers over human teachers, such as the scarcity of qualified teachers and their cost-effectiveness. They also suggested that the role of the teacher could be better performed using AI tools in the future. Conversely, they also pointed out that the need for skills such as emotional intelligence, creativity and communication, which are naturally endowed by human teachers, good teachers continue to remain a need in future classrooms.
Thus, the employment of technological advancements, such as AI/interactive tools in the area of education, specifically teaching-learning and research, poses several opportunities and challenges to various stakeholders. Although previous studies have pointed toward the visible advantages and potential threats of AI/interactive tools such as ChatGpt to education, there is a need for further exploration in this emerging area. Therefore, the present study is an attempt is been made to determine the factors influencing the use of AI/interactive tools such as chatbots in education and research. Based on these aspects, the following research questions were framed:
What factors influence the use of AI/conversational tools in education and research?
What is the level of influence of factors on the usage of AI/conversational tools in education and research?
To address the above research questions, the present study aimed to analyze the perceptions of academicians who are teachers and researchers regarding various aspects of the use of AI/chatbots-based technologies in education and research.
3. Methodology
The primary objective of the present investigation is to assess academics’ perspectives on the utilization of AI and chatbot-based technologies in the realms of education and research. Many studies (Hider and Pymm, 2008; Connaway and Powell, 2010; Blumberg et al., 2014) have recommended a survey method to evaluate the user's perception of any products, services and technology. The present study was based on both primary and secondary data. The primary data were collected through an online survey. To achieve this objective, the study adheres to a specific methodology comprising the following steps:
Identification and selection of the domain to be studied.
Generation of items through literature review and focus group discussions with experts.
Classification of items into separate categories.
Initial pilot survey to test feasibility of the instrument.
Validation of the instrument by subject matter experts.
Pilot testing of the instrument to ensure its effectiveness.
Finalization of the instrument through modification and refinement.
Collection of data related to the research instrument developed to evaluate the use of AI tools in education and research.
Exploratory factor analysis was used to determine the structure of the instrument.
Assessment of the instrument's internal consistency and validity through reliability and validity.
Data analysis and interpretation.
3.1 Item generation
Following the finalization of the domain AI tools in education and research, the next procedure followed in the study was the development of items that could assess the construct based on the objective of the study. There are many scientific procedures to follow in the process of item development (Mumford et al., 1996). This study used earlier literature and focus group discussions with a diverse group of experts in the fields of education and research (Nyumba et al., 2018). Academicians and researchers are considered primary respondents because they are well versed with the ground reality of environmental education development. Therefore, focus group discussions consist of academics and researchers from different domains such as humanities, science and commerce streams.
Focus group discussions began by highlighting the aim of the study and the significance of the discussions. The discussion guidelines used during the discussions were shared with experts. Insights from the discussions were systematically recorded with the help of a moderator. After the conclusion of the focus group discussions, the recorded insights through notes were examined to identify the concept to which insight is related. After classifying the notes, academic experts were consulted to verify the relevance and accuracy of the classifications. Upon completion of this process, statements concerning the factors influencing the adoption of AI tools for academics and research were developed.
Initially, 33 items were constructed and grouped into various categories (awareness, benefits, opportunities, challenges, risks, sustainability and the ethical aspects of AI tools in education and research). The items and their sources are listed in Table 1. for the questionnaire, but after the focus group discussion, 7 items were deleted and finally, 26 items were included in the survey instrument. In the next stage, an additional set of discussions with experts was conducted to re-verify whether any of the items were repeated on the same theme, after which they finalized to retain all 26 items among the said categories. Finally, there were two parts to the questionnaire. The first part consisted of the demographic and educational background of the respondents. The second part consisted of the 26 statements discussed above, for which respondents were asked to rate their insights for each dimension pertaining to the use of AI tools for education and research purposes on a five-point Likert scale (1 = Strongly Disagree to 5 = Strongly Agree).
3.1.1 Initial pilot survey.
After developing the questionnaire, a pilot survey was conducted with 33 respondents. A draft questionnaire was finalized and included questions pertaining to demographic aspects related to the use of AI tools in education and research. In the next stage, three experts were consulted for their feedback. The experts recommended continuing the questionnaire with minor modifications related to sentence formation and interchange of items between the said dimensions; subsequently, the questionnaire was modified accordingly.
3.1.2 Data collection procedure.
Since the topic of this study is new and emerging, data cannot be gathered through secondary sources. Hence, the survey method was used to collect primary data. The survey questionnaire was included in Google forms and distributed among academics and researchers using a convenience sampling method. Convenience sampling was used because of the difficulty in accessing the respondents under consideration for the study (Emerson, 2015). Various platforms such as LinkedIn, University websites and websites of professional associations, such as the Indian Commerce Association and Indian Accounting Association, were used to mobilize the list of respondents and their e-mail ids. A total of 226 e-mail ids from academics and researchers were gathered. Google forms were distributed to the respondents through e-mail ids obtained. Real-time collection of responses was performed through a pre-established timeline.
The e-forms were distributed on July 15, 2023, and opened until August 31, 2023. Periodic reminders were sent weekly to enhance the response rates. After gathering the responses, a detailed and careful examination and curation of the data were performed to eliminate issues of completeness, accuracy and reliability. Duplicate and incomplete responses were excluded from the database. The final database on the use of AI in education and research was transported to a compatible format for further analysis and interpretation in relation to the objective of the study.
3.2 Demographic profile of respondents
Using Google Forms, a questionnaire was circulated among 430 academicians in various domains teaching at the UG and PG levels. Of the circulated questionnaires, 193 responses were received, of which 177 completed responses at a rate of 41.16%, all of which were considered for further analysis.
The profile of the respondents (Table 2) was diversified. Eighty-five respondents were male (%) and 92 respondents were female (5), with 43 respondents belonging to the age category of below 25 years (24.29%), 113 respondents between the ages of 25 and 35 years (26.27%), 15 respondents between the ages of 35 and 40 years (8.47%), 3 respondents between the age category of 45 and 55 years (1.69%) and 3 respondents were under the age of 55 and above (1.69%).
Furthermore, 46 respondents were PhD holders (25.98%), 110 respondents were Post Graduates (62.14%) and 21 respondents were Professional Graduates (11.86%). In terms of experience, 77 respondents had less than 5 years of experience (43.50%), 68 respondents are under the experience category of 5 to 10 years (38.41%) and 32 respondents are under the experience category of 10 to 15 years of experience (18.07%).
In terms of roles, 93 respondents were teachers (52.54%), 34 respondents were researchers (19.20%) and 50 respondents were working in both the teaching and research categories (28.24%).
In terms of the Teaching Domain, 74 respondents belonged to Social Sciences (41.80%), 30 respondents belonged to Commerce and Business (16.94%) and 73 respondents belonged to the science domain (41.2%).
3.3 Research instrument and its reliability and validity
The research instrument included a 26-item questionnaire designed with the help of focus group interviews with experts in the field of artificial intelligence (AI) and education for validation. The designed questionnaire was first administered as a pre-test to 33 respondents, and the questionnaire was incorporated with the feedback, distributed and the data were gathered. To assess the item reliability, 33 questionnaires were completed and Cronbach’s alpha coefficient was considered equal to 0.813, suggesting that the research instrument is acceptable for use in data collection.
3.4 Data analysis tools
This study attempted to use scientific tools to analyze the collected data in relation to the objectives of the study. To find the underlying variables of the summarized data set, the factor analysis method was adopted. The initial data for the factor analysis was a variable matrix. The factor analysis has no predetermined dependent variables, and the goal of the exploratory analysis is to sum up the data. Principal components analysis was used, in which the total variance of the observed variables was analyzed. The significance of the chi-square statistic and Bartlett’s test is an essential condition for moving forward with factor analysis (Kokoska et al., 1989). The KMO test assesses overall sampling adequacy, while Bartlett’s test examines the null hypothesis of no correlation among the variables (Pallant, 2011). The researchers have used these tests in combination to make a decision about proceeding with factor analysis.
A principal component analysis and extraction method with a Varimax with Kaiser Normalization rotation was used to determine the factor loading and commonalities. From the results of the EFA, a multivariate regression model was developed to identify the key factors influencing the use of AI/ChatGPT/Chatbot technologies in education and research.
The study also used multiple regression and ANOVA techniques to assess the influence of independent variables on the dependent variables and their variances.
4. Results
Results of the Kaiser-Meyer-Olkin measure of sampling adequacy (Pallant, 2011). Furthermore, Bartlett’s test of sphericity was conducted to check whether the correlation matrix fits an identity matrix and results showed (Table 3) a chi-square value that was significant (chi square = 4398.843, p < 0.001), meaning that the correlation matrix is significantly different from the identity matrix and is suitable for conducting factor analysis.
Factor analysis yielded seven factors, accounting for 76.83% of the cumulative variance. This indicates that the factors extracted from the data captured nearly 79% of the variability observed in the original variables. This percentage is often considered substantial, indicating that the factors identified are meaningful in explaining the underlying patterns in the data (Ananda and Devesh, 2018).
The results of the factor analysis are shown in Table 3 and interpreted as follows: the first factor, “Awareness of AI-based Technology Usage in Education,” consists of items 4, 5 and 9. This factor accounted for 16.461% of the total variance. Students appear to believe that their awareness of AI-based technology influences their education. The second factor, “Benefits of AI/Conversational tools (ChatGPT) Usage in Education,” contained items 1, 2, 6, 8 and 10, accounting for 14.973% of the total variance. The third factor, “Opportunities of AI/Conversational tools (ChatGPT) Usage in Education,” consisted of items 7, 18, 23, 24 and 26, accounting for 12.871%. The fourth factor, “Challenges of AI/Conversational Tools (ChatGPT) Usage in Education,” consisted of item 3,4 20 and 21 and accounted for 9.554% of the total variance. The fifth factor, “Sustainability of AI/Conversational tools (ChatGPT) Usage in Education,” consisted of items 11, 12 and 17 and accounted for 9.201% of the total variance. The sixth factor, “Ethical aspects of AI/Conversational tools (ChatGPT) usage in Education” consists of item 13,14 and 22 and accounts for 8.137% of the total variance. The seventh factor, ethical aspects of AI/conversational tools (ChatGPT) usage in Education includes 25, 28 and 29 accounting for a variance of 5.635%.
The factor loadings (Table 4) show the strength and direction of the relationship between each variable and the factors, and all values were above 0.5. Communalities refer to the proportion of variance in an observed variable that is accounted for by the underlying factors. Therefore, each observed variable has a corresponding communality value that represents the shared variance between the variable and extracted factors, and most of the commonalities are indicated to be strong as they are closer to 1 instead of 0.
The scree plot shows (Figure 1) a bend in the curve at a factor of 8. Consequently, seven factors were extracted. These seven factors explained most of the variance (also see Table 5).
The factors derived using exploratory factor analysis were used to derive their influence on AI/ChatGPT/Chatbot technologies in education and research. Hence, multiple regression analysis was applied. Prior to using multiple regression analysis and ANOVA, the Shapiro-Wilk test was used to test the normality of the data and the p-value from the Shapiro-Wilk test (0.073) proved that the data were normal.
The R Square in a multiple regression analysis (Table 6) represents the explained variance that can be attributed to all predictors in a progression. The predictors of the level of use of AI/ChatGPT/chatbot technologies are awareness, benefits, challenges, opportunities, sustainability, risks and ethical aspects. R-squared gives explanatory power. In Table 6 The model summary shows an R-squared value of 0.518. This indicates that 51.8% of the variance in the dependent variable (level of usage) was explained by awareness, benefits, challenges, opportunities, sustainability, risk and ethical aspects.
Table 7 reveals the results of multiple regression analysis were statistically significant (F-statistic = 25.955; degrees of freedom = 7, 169; p =,000 or p < 0.001). Hence, this indicates that the independent variables explain the model well, considering the model to be a good fit.
4.1 Factors influencing the use of artificial intelligence/ChatGPT/chatbots technologies in education and research
A linear multiple regression model was formulated to assess the factors influencing the use of AI/ChatGPT/Chatbot technologies in education and research.
Level of usage of AI/ChatGPT/Chabot = Constant+ β1 Awareness + β2 Benefits + β3 Opportunities + β4 Challenges + β5 Risks + β6 Ethical aspects + β7 Sustainability + β error.
Examination of the regression coefficient reveals that awareness of AI/ChatGPT/chatbot technology significantly impacts the level of usage in education and research (β = 0.612, t = 8.186, p < 0.001). The regression coefficients for benefits (β = 0.556, t = 7.022, p < 0.001) and opportunities (β = 0.525, t = 4.084, p < 0.001) both have a positive impact on the level of usage of such technologies in education and research. In contrast, the regression coefficients are negative for challenges (β = −0.399, t = −3.295, p < 0.001) and sustainability (β = −0.502, t = −3.947, p < 0.001) factors that convey a negative significant impact on the perceived level of usage of AI/ChatGPT/Chatbot for education and research. However, risk and ethical factors did not have any significant impact on the perceived level of usage of AI/ChatGPT/Chatbot for education and research. Overall, multiple regression analysis has proved that awareness, benefits, opportunities, challenges and sustainability are influential factors for the perceived level of usage of AI/ChatGPT/Chatbot for education and research in the modern academic environment.
When predictors are correlated, the standard errors of their coefficients tend to increase, thereby inflating their variance. VIF serves as a tool to quantify the extent to which variance inflation occurs. The VIF values in Table 8 are all less than five, indicating the nonexistence of multicollinearity (Debbie and Victoria-Feser, 2023).
5. Discussion
The primary purpose of this study was to determine the factors that influence the adoption of AI/ChatGPT in education and research. The study was conducted using a survey method and used scientific procedures with the help of factor analysis, multiple regression analysis and ANOVA.
In relation to the first research question, the factors influencing the perception of academic staff regarding the use of AI/ChatGPT/Chatbot were identified using principal component analysis (PCA) with varimax rotation. The rule of minimum eigenvalue (1.0) was applied, and only those items whose factor loadings were at least 0.30 in PCA were selected. Seven factors–Challenges, Benefits, Awareness, Opportunities, Risks, Sustainability and Ethical considerations–were identified. The justification is provided by the seven pertinent dimensions extracted using the principal component method of factor analysis. The study revealed seven factors (Challenges, Benefits, Awareness, Opportunities, Risks, Sustainability and ethical consideration) that significantly influence the adoption of AI/ChatGPT/Chatbot in education and research. The total variance of 76.8% for an eigenvalue greater than one sufficiently proves the significance of the dimensions, and the remaining 19.1% of the variance is explained by other variables. Among all these factors, challenges account for 16.461%, which is considered to be the most important factor influencing the adoption of such technologies in education and research, followed by benefits (14.973%), awareness (12.871%), opportunities (9.554%), risks (9.201%), sustainability (8.137%) and ethical consideration (5.635%). The success or failure to adopt such technologies depends on the level of awareness among the academic and research communities (Yu et al., 2024). The use of AI/ChatGPT/Chatbot in education and research poses many challenges, such as reduced creativity among both learners and instructors, making them obsolete because of their non-dependence on real knowledge resources such as reference books. Therefore, challenges in the use of such technologies in education and research are considered a prominent factor (Adeshola and Adepoju, 2023). Even though there are many challenges, such technology offers many benefits for academia and research, such as ease of adoption of technology and digital skills in education (Mijwil et al., 2023). They also assist in offering simulated learning platforms in a remote learning environment; such technologies help both learners and instructors access more customized resources (Sok and Heng, 2023; Al-Obaydi et al., 2023). Hence, the benefits of using such technologies are one of the factors under consideration in this study. The results of the study also confirmed that the opportunities and risks created by the use of AI/ChatGPT/Chatbots are motivating factors in inculcating such tools in academic and research environments (Dai et al., 2023; Bahrami et al., 2023; Wang et al., 2024). Another factor identified in this study that influences the use of AI and allied tools in education and research is that the sustainability of such tools in education and research because such tools may negatively influence the duplication of research work, unethical paraphrasing and defining the scope of its usage matters to its sustainability (Mageira et al., 2022; King, 2023). Ethical aspects are another prominent, debatable and researchable factor identified by the study. The use of AI/ChatGPT/Chatbots in academics and research matters of integrity, sanctity and protection of the interest of original researchers. Therefore, before implementing such tools these aspects have to be taken into consideration (Broyde, 2023; Casheekar et al., 2024).
In relation to the second research question, the study performed a multiple regression analysis, which proved that awareness, benefits, opportunities, challenges and sustainability have a significant positive influence on the perceived level of usage of chatbot/AI or Chat GPT for education. This observation reinforces the findings of studies such as Borenstein and Howard (2021), Qadir (2022), Sullivan et al. (2023), Cotton et al. (2023) and Rahman and Watanobe (2023). The other two factors–risk and ethical considerations–had a negative impact on the perceived level of usage of AI/ChatGPT/Chatbot in education and research in an academic environment. This observation was consistent with the findings of Lindner et al. (2019), Chounta et al. (2022), Lee and Perret (2022), Halaweh (2023).
In summary, the study suggests that regulatory authorities, educational institutions and academic communities should consider these factors before the adoption of AI and allied tools in education and research.
6. Implications of the study
This study has the following significant practical implications:
6.1 Strategies for overcoming the challenges of artificial intelligence tools
Educational institutions and regulatory authorities should develop strategies to overcome the challenges highlighted in this study associated with the use of AI tools in education and research. These strategies should help reduce creativity and overreliance on AI tools. These strategies may consist of creating guidelines for the appropriate use of such tools as complements rather than replacing traditional methods of learning.
6.2 Awareness and training
The outcome of this study suggests that prospective promoters and adopters of such tools are involved in increasing awareness and training for both educators and researchers on the proper use of AI tools. This will maximize the benefits of using such tools, while minimizing the risks and ethical concerns highlighted in the study.
6.3 Need for the development of ethical frameworks
Educational institutions and regulatory bodies should establish comprehensive ethical guidelines for the use of AI tools in the academic environment. Such guidelines should address the issues highlighted in the study, such as academic integrity, proper citation and scope of usage.
6.4 Need for sustainable integration
The implications of the study also suggest that educational institutions should consider long-term sustainability aspects, such as regularly assessing the impact of learning outcomes, updating curricula to inculcate AI literacy and maintaining a balance between technological advancement and traditional educational values.
7. Conclusion
AI/interactive tools such as ChatGpt/Chatbots are part of the ever-evolving landscape of education and research. The adoption of this in the academic and research environments is influenced by many factors, as discussed above. The significant factors influencing the perception of the academic and research community toward the adoption of AI/interactive tools, such as Chatbots/ChatGpt for education and research, are challenges, benefits, awareness, opportunities, risks, sustainability and ethical considerations. Among these factors, the challenges involved in the use of AI tools emerged as the most influential, highlighting the need to address the obstacles and difficulties associated with these tools in education and research contexts. Other factors, such as benefits, awareness, opportunities and sustainability of such tools in the education and research domain, also positively impacted the perceived usage of such tools by emphasizing the potential advantages and opportunities they bring to academia and research. However, risks and ethical considerations related to the use of AI tools in education and research have a negative influence on the perceived usage of such technologies. This assists in addressing issues concerning risks and ethical aspects, which is essential for fostering a friendly environment for the implementation of these technologies in academics and research.
The major limitations of this study are that it used only academics, and a more diverse sample could provide a broader perspective on the issue involved. Furthermore, the findings may not be entirely generalizable to all education and research institutes because the factors influencing the use of these tools can vary accordingly. Future studies may focus on undertaking longitudinal studies to evaluate how these factors emerge over time and analyze whether there are any changes in perception that lead to varied adoption patterns of AI/interactive tools for education and research. In addition, researchers may also study how these factors vary across different academic and research environments across the globe, which could provide fruitful insights into the global adoption of these tools in academia and research. As the application of AI/interactive tools is new to the academic and research environments, future researchers may also focus on undertaking qualitative research to validate the findings of the present study, which could offer an in-depth understanding of specific aspects concerning the factors identified by the present study.
The major contribution of the present study is that it adds value to the existing literature on AI/Interactive tools adoption in academia and research by offering a quantitative analysis of the factors impacting teachers' perception of the usage of such tools. Furthermore, the outcome of the study has practical implications in that it stressed the importance of resolving challenges and enhancing awareness and benefits while carefully mitigating risks and ethical concerns in the integration of technology within the educational and research environment. These insights can assist policymakers in making decisions and strategies to efficiently adopt AI tools in academia and research to enhance the overall quality of learning experiences.
More importantly, the outcome of the study also indirectly contributes to achieving the UNSDGs in terms of UNSDG-4 (quality education), and educational institutes can make informed decisions to improve the quality of education and promote lifelong learning opportunities by considering the factors identified by the study. Further, the study also indirectly helps achieve UNSDG-9 by providing insights into the challenges, benefits and opportunities of AI tools for education and research to foster innovation and technological advancements in the education environment. By considering the factors identified in this study, educational institutions can address inequalities by providing a more equitable distribution of academic resources to promote high-quality education. This supports the UNSDG-10(reduced inequalities). By studying the outcomes of the study, educational institutes, government agencies and technology developers can collaborate for responsible implementation of such technologies in education and research. This outcome is in line with UNSDG-17, that is, partnership of sustainable development.
Figures
Figure 1.
Scree plot
Items and their sources
| Dimensions | Items | Code | Source |
|---|---|---|---|
| Awareness of AI-based technology usage in education | Q4) Are you aware of AI/Conversational tools (ChatGPT) usage in the context of education? | AW1 | Wang et al., 2023 |
| Q5) Have you ever experienced AI/Conversational tools (ChatGPT) usage in teaching and learning in education? | AW2 | Chounta et al., 2022 | |
| Q9)What is your perceived level of satisfaction from the experience of using AI/Conversational tools (ChatGPT) for education? | AW3 | Yu et al., 2024 | |
| Benefits of AI/conversational tools (ChatGPT) usage in education | Q1) With the help of AI/Conversational tools (ChatGPT) designing and integrating of technology and digital skills in education is easy. (Functional and decision-making) | BEN1 | Mijwil et al., 2023 |
| Q2) AI/Conversational tools (ChatGPT) for education is more beneficial to those teachers and learners who are in remote learning environments than a classroom learning environment | BEN2 | Sok and Heng, 2023 | |
| Q6) In AI/Conversational tools (ChatGPT) based education teachers and learners will get access to more customized study materials/cases/illustrations/practical issues | BEN3 | Al-Obaydi et al., 2023 | |
| Q8) AI/Conversational tools (ChatGPT) are more beneficial in solving students’ queries on academic and non-academic (administrative aspects) in education | BEN4 | Bilquise et al., 2023 | |
| Q10) It is more useful to both learners and instructors to get academic resources that are not available in libraries or educational institutions in an efficient and timely way | BEN5 | Hider and Pymm, 2008 | |
| Opportunities of AI/conversational tools (ChatGPT) usage in education | Q7) Preparation of customized study materials/assignments can be smoother with the help of the AI/Conversational tools (ChatGPT) | OP1 | Bahrami et al., 2023 |
| Q18) AI/Conversational tools (ChatGPT) help to ensure 24x7 hours tutor in the execution/completion of projects as part of education | OP2 | Wang et al., 2024 | |
| Q23) AI/Conversational tools (ChatGPT) become transformational platforms than Google to acquire required resources for both teachers and students | OP3 | AlAfnan et al., 2023 | |
| Q24) The use of these technologies in education and research is more beneficial to educators who are facing a workload burden due to lack of time | OP4 | Tülübaş et al., 2023 | |
| Q26) The translation by AI/Conversational tools (ChatGPT) helps both teachers and students to focus more on content/subject matter rather than technical aspects such as translating, searching dictionaries etc., | OP5 | Baidoo-Anu and Ansah, 2023 | |
| Challenges of AI/conversational tools (ChatGPT) usage in education | Q3) AI/Conversational tools (ChatGPT) impacting more negatively than the pandemic on the education and research environment | CH1 | Borenstein and Howard, 2021 |
| Q4) Both teachers and students may become lazy/less creative due to the existence of AI/Conversational tools (ChatGPT) while preparing to perform their jobs | CH2 | Celik et al., 2022 | |
| Q20)As AI/Conversational tools (ChatGPT) are more interactive and responsive in less time or zero time both learners and instructors may run away from real knowledge resources like reference books | CH3 | Adeshola and Adepoju, 2023 | |
| Q21) AI/Conversational tools (ChatGPT) technologies to be employed in education need to be trained on diverse data sets such as from different nations and in different languages otherwise irrelevant output will be generated | CH4 | Aleedy et al., 2022 | |
| Risks AI/conversational tools (ChatGPT) usage in education | Q11) Banning AI/Conversational tools (ChatGPT) in education at the institutional level makes the institutional position will be irrelevant | RI1 | Dai et al., 2023 |
| Q12) Regulators are required to monitor the developers of AI/Conversational tools (ChatGPT) in the context of education and research to protect integrity in education | RI2 | Cotton et al., 2023 | |
| Q17) There is a need for collaboration between the government and B’schools to determine the appropriate use of AI/Conversational tools (ChatGPT) in education otherwise misuse will be more | RI3 | Casheekar et al., 2024 | |
| Sustainability of AI/conversational tools (ChatGPT) usage in education | Q13) Duplication of research work in different languages may increase due to AI/Conversational tools (ChatGPT) | SUS1 | Mageira et al., 2022 |
| Q14) Unethical paraphrasing cannot be traced unless otherwise, these technologies are more advanced in this context | SUS2 | King, 2023 | |
| Q22) Defining the scope of use of AI/Conversational tools (ChatGPT) in research is difficult and it needs a global framework like COPE guidelines | SUS3 | Aleedy et al., 2022 | |
| Ethical aspects of AI/conversational tools (ChatGPT) usage in education | Q25) The importance, integrity and sanctity are questioned when researchers prepare and publish for number's sake through AI/Conversational tools (ChatGPT) and other technologies | ETH1 | Broyde, 2023 |
| Q28) Protection of the interest of original researchers is also a question in the AI/Conversational tools (ChatGPT) research environment | ETH2 | Casheekar et al., 2024 | |
| Q29) Creativity, cognitive skills and researcher involvement in the research may deteriorate if the researchers rely more on AI/Conversational tools (ChatGPT) | ETH3 | Fiialka et al., 2023 |
Source: Authors’ creation
Demographic profile of respondents
| Demographic details | No. of respondents | % |
|---|---|---|
| Gender | ||
| Male | 85 | 48.3 |
| Female | 92 | 51.7 |
| Total | 177 | 100 |
| Age (years) | ||
| Below 25 | 43 | 24.3 |
| 25 to 35 | 113 | 63.8 |
| 35 to 45 | 15 | 8.5 |
| 45 to 55 | 3 | 1.7 |
| Above 55 years | 3 | 1.7 |
| Total | 177 | 100 |
| Education | ||
| Ph.D | 46 | 26 |
| Post graduate | 110 | 62.1 |
| Professional studies | 21 | 11.9 |
| Total | 177 | 100 |
| Age of service (years) | ||
| Less than 5 | 77 | 43.5 |
| 5–10 | 68 | 38.42 |
| 10–15 | 32 | 18.08 |
| Total | 177 | 100 |
| Role in education | ||
| Teaching | 93 | 52.5 |
| Research | 34 | 19.3 |
| Both | 50 | 28.2 |
| Total | 177 | 100 |
| Teaching domain | ||
| Social sciences | 74 | 41.8 |
| Commerce and business | 30 | 16.9 |
| Science | 73 | 41.2 |
| Total | 177 | 100 |
Authors’ calculations
KMO and Bartlett's test
| Kaiser-Meyer-Olkin measure of sampling adequacy | 0.685 |
| Bartlett's test of sphericity approx. Chi-Square | 4398.843 |
| Degrees of freedom | 351 |
| Significant | 0.000 |
Authors’ compiled
Factor analysis
| Dimensions | Items | Factor loadings | Commonalities |
|---|---|---|---|
| Awareness of AI-based technology usage in education | AW1 | 0.814 | 0.706 |
| AW2 | 0.784 | 0.641 | |
| AW3 | 0.710 | 0.716 | |
| Benefits of AI/conversational tools (ChatGPT) usage in education | BEN1 | 0.686 | 0.681 |
| BEN2 | 0.556 | 0.672 | |
| BEN3 | 0.636 | 0.689 | |
| BEN4 | 0.573 | 0.671 | |
| BEN5 | 0.580 | 0.646 | |
| Opportunities of AI/conversational tools (ChatGPT) usage in education | OP1 | 0.814 | 0.858 |
| OP2 | 0.769 | 0.712 | |
| OP3 | 0.572 | 0.845 | |
| OP4 | 0.771 | 0.733 | |
| OP5 | 0.520 | 0.764 | |
| Challenges of AI/conversational tools (ChatGPT) usage in education | CH1 | 0.559 | 0.814 |
| CH2 | 0.645 | 0.843 | |
| CH3 | −0.563 | 0.851 | |
| CH4 | 0.814 | 0.689 | |
| Risks AI/conversational tools (ChatGPT) usage in education | RI1 | 0.830 | 0.682 |
| RI2 | 0.892 | 0.882 | |
| RI3 | 0.837 | 0.853 | |
| Sustainability of AI/conversational tools (ChatGPT) usage in education | SUS1 | 0.853 | 0.787 |
| SUS2 | 0.699 | 0.789 | |
| SUS3 | 0.807 | 0.833 | |
| Ethical aspects of AI/conversational tools (ChatGPT) usage in education | ETH1 | 0.804 | 0.786 |
| ETH2 | 0.831 | 0.791 | |
| ETH3 | 0.508 | 0.559 |
Authors’ compiled
Total variance explained
| Component | Initial eigenvalues | Extraction sums of squared loadings | Rotation sums of squared loadings | ||||||
|---|---|---|---|---|---|---|---|---|---|
| Total | % of variance | Cumulative % | Total | % of variance | Cumulative % | Total | % of variance | Cumulative % | |
| 1 | 9.031 | 34.733 | 34.733 | 9.031 | 34.733 | 34.733 | 4.280 | 16.461 | 16.461 |
| 2 | 2.920 | 11.230 | 45.962 | 2.920 | 11.230 | 45.962 | 3.893 | 14.973 | 31.435 |
| 3 | 2.187 | 8.411 | 54.373 | 2.187 | 8.411 | 54.373 | 3.346 | 12.871 | 44.305 |
| 4 | 1.812 | 6.971 | 61.343 | 1.812 | 6.971 | 61.343 | 2.484 | 9.554 | 53.859 |
| 5 | 1.603 | 6.167 | 67.510 | 1.603 | 6.167 | 67.510 | 2.392 | 9.201 | 63.060 |
| 6 | 1.367 | 5.256 | 72.766 | 1.367 | 5.256 | 72.766 | 2.116 | 8.137 | 71.196 |
| 7 | 1.057 | 4.065 | 76.832 | 1.057 | 4.065 | 76.832 | 1.465 | 5.635 | 76.832 |
| 8 | 0.906 | 3.485 | 80.317 | ||||||
| 9 | 0.765 | 2.943 | 83.260 | ||||||
| 10 | 0.668 | 2.569 | 85.828 | ||||||
| 11 | 0.588 | 2.261 | 88.089 | ||||||
| 12 | 0.507 | 1.948 | 90.037 | ||||||
| 13 | 0.448 | 1.724 | 91.761 | ||||||
| 14 | 0.339 | 1.304 | 93.065 | ||||||
| 15 | 0.325 | 1.249 | 94.315 | ||||||
| 16 | 0.277 | 1.065 | 95.380 | ||||||
| 17 | 0.241 | 0.926 | 96.306 | ||||||
| 18 | 0.214 | 0.822 | 97.128 | ||||||
| 19 | 0.166 | 0.639 | 97.767 | ||||||
| 20 | 0.142 | 0.545 | 98.312 | ||||||
| 21 | 0.121 | 0.464 | 98.776 | ||||||
| 22 | 0.107 | 0.412 | 99.188 | ||||||
| 23 | 0.081 | 0.311 | 99.499 | ||||||
| 24 | 0.059 | 0.227 | 99.727 | ||||||
| 25 | 0.047 | 0.183 | 99.910 | ||||||
| 26 | 0.024 | 0.090 | 100.000 | ||||||
Extraction Method = principal component analysis
Source: Authors’ Compiled
Results of multiple regression analysis
| Model | R | R square | Adjusted R square | Std. Error of the estimate |
|---|---|---|---|---|
| 1 | 0.720a | 0.518 | 0.498 | 0.531 |
aPredictors: (Constant), awareness, benefits, challenges, opportunities, sustainability, risk, ethical aspects
Source: Authors’ compiled
Results of ANOVA test
| Model | Sum of squares | df | Mean square | F | Sig. | |
|---|---|---|---|---|---|---|
| 1 | Regression | 51.147 | 7 | 7.307 | 25.955 | 0.000b |
| Residual | 47.576 | 169 | 0.282 | |||
| Total | 98.723 | 176 | ||||
aDependent Variable = perceived level of usage of AI/ChatGPT/Chatbot technologies in education and research.
bPredictors = (Constant), awareness, benefits, challenges, opportunities, sustainability, risk, ethical aspects
Source: Authors’ compiled
Multiple regression results
| Model | Unstandardized coefficients |
Standardized coefficients | t-value | p-value | VIF (variance inflation factor) | |
|---|---|---|---|---|---|---|
| B | Std. Error | β | ||||
| (Constant) | −6.246 | 1.021 | −6.120 | 0.000 | ||
| Awareness | 0.175 | 0.021 | 0.612 | 8.186 | 0.000 | 1.00 |
| Benefits | 0.082 | 0.012 | 0.556 | 7.022 | 0.000 | 1.05 |
| Opportunities | 0.166 | 0.041 | 0.525 | 4.084 | 0.000 | 2.00 |
| Challenges | −0.045 | 0.014 | −0.399 | −3.295 | 0.001 | 1.50 |
| Risks | −0.032 | 0.024 | −0.118 | −1.337 | 0.183 | 4.00 |
| Ethical aspects | −0.059 | 0.047 | −0.156 | −1.261 | 0.209 | 2.58 |
| Sustainability | −0.161 | 0.041 | −0.502 | −3.947 | 0.000 | 3.40 |
The dependent variable is the perceived level of usage of AI/ChatGPT/Chatbots in education and research
Source: Authors’ compiled
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Further reading
Korzynski, P., Mazurek, G., Altmann, A., Ejdys, J., Kazlauskaite, R., Paliszkiewicz, J., Wach, K. and Ziemba, E. (2023), “Generative artificial intelligence as a new context for management theories: analysis of ChatGPT”, Central European Management Journal, Vol. 31 No. 1, pp. 3-13, doi: 10.1108/CEMJ-02-2023-0091.
Thu, C.H., Bang, H.C. and Cao, L. (2023), “Integrating ChatGPT into online education system in Vietnam: opportunities and challenges”, pp. 1-3.
Acknowledgements
Swamy koragajja in his blessings the work has been completed and the authors also thankful to the “Quality Education for All” journal’s editorial team and reviewers. The authors also grateful to note the support of master likhesh in completing this research work.