Student Perceptions of AI Integration in Accounting Education: Exploring the Value, Challenges, and Career Readiness

Abstract

This study examines accounting students’ perceptions of artificial intelligence (AI) integration in their education, focusing on its value, challenges, and influence on career readiness. A quantitative survey assessed students’ views on AI’s relevance, difficulties in learning and applying it, and its role in enhancing professional preparedness. Results show that most students see AI as beneficial for future careers, especially in strengthening data analysis, problem-solving, and technical skills. However, they face challenges such as limited exposure to real-world AI tools, a lack of hands-on experience, and insufficient instructor expertise. These issues point to the need for structured learning and faculty development. Despite the obstacles, students, particularly undergraduates, express confidence in AI’s positive impact on their career prospects. The study recommends curriculum improvements, including more practical applications, enhanced faculty training, and a balance between technical skills and creative thinking. These findings support aligning AI education with student needs and evolving industry expectations.Bloom framework can add value, in different ways, to instructional design and assessment.

Introduction

Artificial intelligence (AI) is revolutionizing the accounting profession by changing the way financial information is processed, analyzed, and utilized for decision-making (Cudia & Legaspi, 2024). AI has transformed the conventional boundaries of accounting by automating tasks such as data entry, reconciliation, and report generation, which allows accounting professionals to concentrate on strategic activities such as forecasting and advisory functions (Bulău et al., 2024). This change mirrors wider industry trends prioritizing technological adoption to drive efficiency, accuracy, and strategic goals. As a result, accounting education has been subjected to a paradigm shift intended to prepare future professionals for this rapidly changing technological context (Țurcan et al., 2024).

The diverse applications of AI across the accounting profession highlight its importance. AI algorithms are also deployed in auditing to detect anomalies and patterns in large volumes of data, enhancing risk assessment and fraud detection (Nusa et al., 2024). AI-powered tools are also transforming tax planning by engaging in a complex analysis of your financials to help plan the best tax strategy while reducing compliance engagement. This trend demands a workforce that can not only understand accounting principles but also harness the power of AI technologies. Accordingly, professional bodies, including the International Federation of Accountants (IFAC), have stressed the importance of AI capabilities in their respective education systems (Thody, 2024). This study aims to fulfill that need by examining students’ perceptions regarding the integration of AI in their accounting education. While there are innumerable articles written about the merit of AI in professional practice, the journey and perspectives of future practitioners on how they feel about these technologies being deployed in their workflows have been less considered. Knowing how students see it is important in terms of aligning the strategies adopted for education with the needs of industry and ensuring that future accountants are best placed to meet those expectations. This study highlights AI integration and three key areas of focus: perceived value, challenge, and the career-ready implications of AI integration.

Another critical area worth investigating is the perceived value of AI integration in accounting education. Supporters of this viewpoint suggest that access to AI tools and techniques provides students with valuable problem-solving skills, analytic thinking, and a grasp of real-world applications (Abdo-Salloum & Al-Mousawi, 2025). Integrating AI-based case studies within the curriculum can be part of the steps in helping students resolve the tension between the theory of literature and the process of individual application. However, if the curriculum does not contextualize the importance of AI for their future careers, not all students will appreciate these benefits (Baldwin-Morgan, 1995). Understanding how students perceive these benefits can help improve such courses’ design and delivery.

However, while the inclusion of AI in core accounting programs may come with tangible benefits, the hurdles to implementation are not insignificant. One of the major challenges is the steep learning curve that comes with learning AI technologies, which can be particularly challenging for students with little or no prior experience in programming or data analytics. (Fülöp et al., 2022). Moreover, differences in institutional resources, including the availability of advanced software and qualified instructors, could further compound these challenges (Nusa et al., 2024). We need to counter not only barriers that exist to teach, but also to ensure that we do not create barriers for diverse student populations.

One other important aspect of this research is the association between AI integration and career preparedness. Matica et al. (2023) noted that the profession of accounting is seeking entrants skilled in AI, data analytics, and other new technologies. For example, research shows that graduates who do more study in AI-related skills are more in demand when introduced to the job market as they are more able to cope with the demands of modern accounting (Hsiao & Lei Han, 2023). Yet the confidence of students in being prepared for AI-infused careers is still a subject of discussion. Other research indicates that insufficient exposure to AI applications diminishes students’ sense of preparedness, despite strong performance in classes (Hsiao & Lei Han, 2023). This study aims to investigate these perceptions and propose a way forward for education and employability.

Given how AI has been growing in importance in accounting education, extensive research has been done to explore the “best” practices of how to integrate these competencies into the curriculum. Researchers have stressed the urgent need for interdisciplinary education that draws together accounting, computer science, and business analytics (Cohen et al., 2023). Additionally, enhance the curriculum with collaborative projects and simulations to enable students to gain both technical skills and soft-skill abilities such as teamwork and communication (Abdo-Salloum & Al-Mousawi, 2025). Moreover, curricula should remain aligned with industry standards and certifications to both enhance students’ marketability and ensure the relevance of their education (Thody, 2024).

This article adds to previous literature by centering students, a group sometimes neglected in conversations about educational innovation. This research aims to offer actionable insights to educators, administrators, and policymakers by exploring students’ perceptions of the integration of AI based on value, challenges, and career readiness. These findings will not only help in curriculum design but will also serve the larger purpose of realigning accounting education to meet the requirements of a technology-driven workforce.

The following chapter of this paper describes the methodological approach taken for this research, which is quantitative, and stipulates that a survey engages with a wide range of students’ perspectives. This solid method guarantees an extensive perception of the reasons affecting student perceptions in addition to laying a beneficial platform for certainty-based suggestions.

Literature Review

The integration of artificial intelligence (AI) into education represents a significant advancement, revolutionizing traditional teaching models into more personalized, adaptive, and efficient systems. AI technologies such as machine learning, natural language processing, and intelligent tutoring systems enhance student engagement, offer tailored feedback, and improve administrative processes (Zawacki-Richter et al., 2019; Peng et al., 2022). Historically, the incorporation of technology into education has been gradual, starting with computer-aided instruction and online learning platforms, and evolving with the rise of AI due to advancements in computational power, big data analytics, and digital resources (Holmes et al., 2019). These developments enable AI to address complex challenges like skill gaps and employability issues.

The Value of AI in Education

AI and Career Readiness

AI in education aligns closely with industry needs, preparing students for increasingly automated and data-driven workplaces (Chan, 2024). Competence in AI tools correlates with improved employability, as businesses prioritize candidates with technical skills and adaptability (Damerji & Salimi, 2021). In fields like accounting, familiarity with AI technologies—such as automated bookkeeping, predictive analytics, and fraud detection—enhances students’ ability to thrive in an evolving profession (Juniardi & Putra, 2024).

Skill Development Benefits

AI fosters the development of critical skills, including analytical thinking, problem-solving, and technical proficiency (Zawacki-Richter et al., 2019). Intelligent tutoring systems and AI-powered simulation platforms provide students with hands-on experience in solving complex problems, particularly valuable in data-intensive fields like accounting (Rojas & Chiappe, 2024). Furthermore, project-based learning environments that incorporate AI stimulate creativity and innovation by encouraging students to explore diverse solutions (Chan, 2024). Virtual workshops and simulations also enhance creative problem-solving skills through immersive, interactive experiences.

Student Perceptions of AI’s Value

Students generally view AI as a beneficial addition to their education. Research indicates that AI tools simplify learning, improve access to resources, and customize instruction (Ravi Kumar & Raman, 2022). Students appreciate AI-driven tools for their ability to enhance engagement and provide real-world applications (Dubey et al., 2023). However, concerns about over-reliance on technology and potential harm to critical thinking skills persist (Strohmier et al., 2024). Addressing these concerns through guided interventions can enhance students’ perceptions of AI’s value.

Challenges of AI Integration in Education

Institutional Challenges

The integration of AI into education faces institutional hurdles such as inadequate infrastructure, high implementation costs, and regulatory issues (Chan, 2024). Many institutions struggle to align AI technologies with existing curricula and assessment standards (Murdan & Halkhoree, 2024).

Instructor-Related Challenges

Instructor readiness is another significant barrier. Many educators lack the training and expertise to effectively use AI tools, limiting their ability to design engaging AI-based coursework (Amado-Salvatierra et al., 2024). Resistance to change and a lack of understanding of AI’s pedagogical potential further hinder integration (Afzaal et al., 2024). Successful AI implementation requires professional development and institutional support to shift teaching methodologies (Holmes et al., 2019).

Student-Identified Barriers

Students often find AI concepts challenging due to perceived complexity and limited access to resources. Many studies report insufficient exposure to AI during their education, which hampers their confidence in using these tools (Baidoo-Anu et al., 2024). Additionally, ethical concerns and a lack of practical applications, such as virtual labs or real-world examples, further complicate AI adoption (Dubey et al., 2023).

AI and Career Readiness in Accounting

The Role of AI in Accounting

The accounting profession is undergoing significant transformation due to AI technologies like machine learning, robotic process automation (RPA), and data visualization tools (Kaur et al., 2023). Machine learning algorithms assist in fraud detection, risk analysis, and predictive modeling, highlighting the need for advanced technical skills (Ibrahim, 2019). Routine tasks like bookkeeping and compliance are increasingly automated, improving accuracy and allowing accountants to focus on strategic decision-making (Greenman et al., 2024).

AI’s Impact on Accounting Roles and Competencies

AI is reshaping accounting job roles and required competencies. Algorithms can analyze large datasets to identify discrepancies and streamline compliance processes, reducing human error (Greenman et al., 2024). Predictive analytics and machine learning tools enhance decision-making and risk assessment (Holmes et al., 2019). The growing adoption of AI in auditing, financial reporting, and tax preparation demands that accountants acquire skills in AI implementation, data analytics, and cybersecurity to remain competitive (Juniardi & Putra, 2024).

Bridging the Gap Between Education and Industry

To prepare students for AI-driven workplaces, educational curricula must align with industry expectations (Damerji & Salimi, 2021). Strategies such as partnerships with accounting firms, internships, and case-based learning models help bridge this gap by providing practical experience alongside theoretical knowledge (Ibrahim, 2019).

Student Preparedness and Confidence

AI-focused education boosts students’ confidence in using AI tools, especially when they engage with practical applications during their studies (Baidoo-Anu et al., 2024). Students with hands-on experience in AI-driven platforms feel better prepared for the workforce (Ravi Kumar & Raman, 2022). However, some students still perceive a disconnect between classroom instruction and real-world applications, underscoring the need for more hands-on learning opportunities and industry-aligned projects (Dubey et al., 2023).

Gaps in Current Research

Limited Focus on Accounting Education

Despite widespread AI integration across disciplines, research specific to accounting education remains scarce (Amado-Salvatierra, 2024). Most studies focus on general education or STEM fields, limiting the development of tailored strategies for accounting students (Kaur et al., 2023; Juniardi & Putra, 2024).

Insufficient Analysis of Student Perceptions

The current literature primarily examines faculty or institutional perspectives, leaving a gap in understanding students’ views on AI integration (Baidoo-Anu et al., 2024). Few studies explore students’ perceptions regarding AI’s value, challenges, and impact on career readiness (Afzaal et al., 2024; Dubey et al., 2023). Addressing this gap is critical for improving AI adoption in accounting education.

Emerging Technologies and Evolving Industry Needs

The rapid evolution of AI technologies poses a challenge for educators to keep pace with industry trends. Existing research often overlooks emerging tools, reducing relevance to current industry needs (Goldman et al., 2024). Further research is needed to assess how new AI technologies influence educational practices and workforce demands (Bobula, 2024).

AI holds transformative potential for education, particularly in accounting, by enhancing career readiness and skill development. However, challenges related to institutional readiness, instructor training, and student engagement need to be addressed for successful integration. Research gaps—particularly the limited focus on accounting education and insufficient analysis of student perspectives—must be bridged to optimize AI adoption. Future studies should explore strategies to align education with evolving industry needs and incorporate emerging technologies, ensuring that students are well-prepared for an AI-driven future.

Methodology

Approach

This study employed a quantitative research approach using a survey to collect data. Surveys are effective tools for gathering standardized information from a large population, enabling the identification of patterns, trends, and relationships within the data. The structured format allows for an efficient comparison of responses, supporting robust statistical analysis.

Participants

The target population for this study consisted of undergraduate accounting students at different academic levels during the Fall 2024 semester at a private, four-year institution in Ohio. The sample size consisted of 119 students with an average of 15 students per class participating in the study. Participants were selected from various accounting courses to ensure a diverse representation of experience and perspectives within the accounting field. Efforts were made to include students from different years of study to capture a comprehensive view of their academic progression and related experiences.

Data Collection Tool

The primary data collection tool was a structured survey designed to capture a range of quantitative data. The survey included a combination of Likert-scale questions, multiple-choice questions, and ranking items. Likert-scale questions assessed participants’ attitudes and perceptions on various topics, while multiple-choice questions gathered factual information and categorical data. Ranking questions enabled participants to prioritize factors or preferences, providing additional insight into their decision-making processes. The survey was distributed electronically to facilitate broad participation and ensure ease of access.

Data Analysis

The survey data collected will be analyzed using statistical methods to identify trends, relationships, and significant differences among variables. Descriptive statistics, such as means, frequencies, and standard deviations, will be used to summarize the data. Inferential statistical tests, including t-tests, ANOVA, and regression analysis, will be employed to examine relationships between variables and differences across groups. The analysis will be conducted using the Statistical Package for Social Sciences (SPSS) to ensure accuracy and reliability in the results. Findings will be interpreted in the context of the research questions to draw meaningful conclusions and inform future research in the field of accounting education.

Results

This data was gathered from a small university in Northwest Ohio. The information came from three sections of ACC 210 (Financial Accounting) or ACC 228 (Managerial Accounting) that are required for all Bachelor of Business Administration (BBA) degree-seeking students and thus included both majors and non-majors. Lastly, the three upper-division courses of ACC 301 (Intermediate Accounting), ACC 313 (Cost Accounting 1), and ACC 403 (Accounting Information Systems) made up the remainder of the sample. If a student was in more than one of these classes, they were asked to take the survey only once to eliminate overlap.

Of the 119 students who were sampled, 85 said that they had taken a course where AI topics were included as part of the curriculum. The descriptive statistics differentiated between 45 accounting majors and 74 non-majors. Sixteen students in this sample were first-year students, 100 were second-year students, 129 were third-year students, and 36 were fourth-year students. One student was identified as a graduate-level student. Not all students answered every question with a valid answer, so sample sizes vary slightly by question.

The research framework contained the structural components of the study concerning the 3 major themes of the value of AI, the challenges of AI, and the impact of AI on career readiness. In addition to demographic questions such as major, year of study at the university, and exposure to AI in coursework, the study focused on four research questions that were then subdivided into eleven specific survey questions. Likert scale ratings, rank orders, and yes/no data were collected for the eleven specific survey questions. The study followed the following outline:

A. Perceived Value of AI in Accounting Education

RQ1 To what extent do students believe that learning AI skills is valuable for their future careers? (Survey 4, 5)

B. Perceived Challenges of AI Integration

RQ2 What challenges do students identify in learning AI as part of their curriculum? (Survey 6, 7)

RQ3 Do students feel they have adequate resources, support, and instruction to understand and apply AI tools? (Survey 8, 9)

C. Impact on Career Readiness and Skill Development

RQ4 Do students feel that AI integration in courses enhances their readiness for their careers? (Survey 10, 11)

Concerning the first major theme on the value of AI, students answered Research Question 1 in the form of survey questions 4 and 5. The surveyed question 4 asked if students felt that AI had value in their future careers. Students answered in the Likert scale format where 1= not valuable, 3= neutral, and 5= very valuable. When separated by the categorical variables of major or non-major year of study within the university, and exposure to AI in the curriculum, the results were tabulated as crosstabs and extended as a Chi-Square goodness of fit to accommodate the nominal variable status of major, year, or the existence of AI in the curriculum. There were no significant differences in proportions between those groupings as is demonstrated in Table 1.

Table 1

Students Believe that Learning AI Skills is Valuable for Their Future Careers
(Survey Question 4)

		Chi-square Goodness of fit by Major (n=119)	Chi-square Goodness of fit by year of study (n=118)	Chi-square Goodness of fit by AI in the curriculum (n=118)
Value of AI	Survey#4	χ2=3.421, p=.331	χ2 =8.706, p=.728	χ2 = 5.410, p=.144
P<.05, *P<.01

Staying within the umbrella of Research Question 1, the survey further explored students’ perception in question #5, which asked students to rank a given skill set that would be most enhanced by learning AI. Students reported a rank ordering of the following skills: problem-solving and critical thinking, data analysis and interpretation, creativity and innovation, technical proficiency and coding skills, and communication and collaboration. When using those ranked categories for the perceived value of AI in the career, a Spearman’s Rho was utilized as it concerns the relationships with the ranking of data. Information gathered on creativity and innovation, data analysis and interpretation, technical proficiency, problem-solving, and communication and collaboration were studied. This information was broken into three specific parts to identify any significant relationships for all information gathered (n = 119), then for relationships for majors only (n = 37), and those for non-majors (n = 50). It was found that significant relationships for all students existed in creativity and innovation with data analysis and interpretation R=-.461**, technical proficiency with problem-solving R=-.462**, technical proficiency with creativity and innovation R=-.371***, and lastly, with communication and collaboration with technical proficiency R=-.213*. Within the majors, significant relationship relationships were found in technical proficiency with problem-solving R=-.538**, creativity and innovation with data analysis and interpretation R=-.383*. Lastly, the results demonstrated significant relationships for non-majors. For this subgroup, technical proficiency related rankings with problem-solving R=-.412***, creativity in innovation with data analysis and interpretation R=-.531**, technical proficiency with creativity and innovation R=-.519**, and communication collaboration with technical proficiency R=-.327**. These results are summarized in Table 2.

Table 2

Students Believe that Learning AI Skills is Valuable for Their Future Careers
(Survey Question 5)

Survey question 5		Creativity and Innovation with Data Analysis and Interpretation	Technical Proficiency with Problem-solving	Technical Proficiency with Creativity and Innovation	Communication and Collaboration with Technical Proficiency
	All N=119 (87 valid entries)	R=-.461**	R=-.462**	R=-.371**	R=-.213*
	Majors (n= 37)	R=-.383*	R=-.538**	R=-.157	R=-.519**
	Non-majors (n= 50)	R=-.531**	R=-.412**	R=-.519**	R=-.327**
P<.05, *P<.01

In the instances where the values provided by the students might also be considered quantitatively by the Likert scale number, comparable results were compiled as insignificant to the population. There were no significant differences in Likert values by major, year, or AI in the curriculum. This information is summarized in Table 4.

Regarding the second theme, challenges of AI integration as perceived by students, survey questions six and seven were identified to answer research question two: students’ perceptions of challenges to learning AI as part of their overall curriculum. The choices provided for ranking included limited access to real-world applications, lack of relevant course content, insufficient practice, inadequate instructor expertise and instruction, and a steep learning curve for AI concepts. For Spearman’s Rho for relationships and ranked challenges as specified in survey question 6, there were significant relationships for all students (n = 101 valid data entries). For example, limited access to real-world applications showed a ranked relationship with a lack of relevant course content R=-.299**, as did insufficient practice with a lack of relevant course content R=-.218*, the learning curve for AI concepts with a lack of relevant course content, R=-.332**. Additionally, inadequate instructor expertise related to limited access to real-world applications R=-.357***. Insufficient practice related to both inadequate instruction R=-.347 ** and with a steep learning curve for AI R=-.331***.

Again, when separated by major, though the sample size was reduced to n = 39, comparable results were found. Here, insufficient practice with a lack of relevant course content showed a rho of R=-.353*, while a steep learning curve for AI concepts with a lack of relevant course content was R=-.335*. Then, inadequate instruct direct expertise related to limited access to real applications R=-.418*and Insufficient practice combined with a steep learning curve had a Spearman’s Rho value of R=-.328*. When analyzed for non-majors only, the sample size was 62 for another series of related ideas in their rank orderings. Here, limited access to real-world applications related to a lack of relevant course content R=-.364** and a steep learning curve for AI concepts with a lack of relevant course content R=-.338*. Inadequate instructor expertise was found to mimic limited access to real-world applications, R=-.302*, and insufficient practice with inadequate instruction, R=-.360. These analyses are summarized in Table 3.

Table 3

Students Identify Learning AI as Part of Their Curriculum (Survey Question 6)

Challenges RQ2 Survey question 6		Limited access to real-world applications with a Lack of relevant course content	Insufficient practice with a Lack of relevant course content	The steep learning curve for AI concepts, with a Lack of relevant course content	Inadequate instructor expertise with Limited access to real-world applications	Insufficient practice with Inadequate instruction	Insufficient practice with a Steep learning curve
	All (n= 101)	R=-.299**	R=-.218	R=-.332**	R=-.357**	R=-.347**	R=-.331**
	Majors (n= 39)	R=-.175	R=-.353*	R=-.335*	R=-.418*	R=-.307	R=-.328*
	Non-majors (n= 62)	R=-.364**	R=-.338*	R=-.338	R=-.302*	R=-.360**	R=.284*
P<.05, *P<.01

Also identified as a perceived challenge of AI integration was survey question number seven, the difficulty in learning AI. Using the Likert scale scores from 1=very easy, 3=neutral, to 5=very hard as quantitative values, the following tests were administered: an independent T for major, an ANOVA for the year of study, and an independent t-test for the existence of AI in the curriculum. No significant findings were demonstrated in any of these analyses, as shown in summary Table 4.

Within the thematic element of challenges of AI integration was research question three: Do students feel like they have adequate resources, support, and instruction to understand and apply AI tools? Here, two additional survey questions were administered. The first was to discern any differences in the perception of the quality of resources to learn AI (1=very poor, 3= neutral, 5= excellent) when separated by major, year of study, or the existence of AI in the student’s curriculum. Again, no significant findings were gathered. Similarly, in survey question number nine, addressing adequate instructor knowledge of AI (1=very poor, 3= neutral, 5= excellent), the same groups discerned by major, your study, and AI in curriculum garnered no significant findings. All the information concerning students’ perceived challenges of AI integration is summarized in Table 4.

Finally, the last theme was the impact of career readiness. Here, survey questions 10 and 11 addressed research question number four: Do students feel that AI integration into their courses enhances their readiness for their careers? The overall summary of research question number 10 was again delineated for major by independent t-test, year students of the study were compared as an ANOVA, and then again by the existence of AI in the curriculum to separate the surveyed students into groups for an independent t-test. As in survey questions 7-9, there were no significant findings. The results are reported in Table 4.

Table 4

Students perceived challenges of AI integration (Survey Questions 7-9)

Table 4Theme	Research Question: Survey#	Independent value by Major (n=115)	ANOVA value by year of study (n= 116)	Independent value by AI in the curriculum (n=117)
Value of AI	RQ1: Survey 5	t=.111, p=.912	F=1.159, p=.333	t=1.305, p=.195
Challenges of AI	RQ2: Survey 7	t = -.129, p=.898	F=.486, p=.746	t= .691, p=.491
	RQ3: Survey 8	t=-.580, p=.563	F=.486, p=.746	t=.632, p=.528
Career Readiness from AI	RQ3: Survey 9	t=-.738, p=.462	F=.486, p=.746	t=-.668, p=.505
	RQ4: Survey 10	t=1.292, p=.199	F=2.076, p=.089	1.318, p=.190
P<.05, *P<.01

Lastly, survey question number 11 asked if students would recommend AI for career readiness. Here, a Chi-square test for independence was appropriate as all the information was qualitative and analyzed as dummy variables where 1 recognized “yes” to recommend or “no” as a 0. The major was similarly coded as a 1 to represent accounting majors or zero for the non-accounting majors. The year of study coded variable applied as an ordinal level of measurement rather than a quantitative measurement as well (i.e., 1= first-year student, 2=2nd year, 3=3rd year, 4=5th year, 5=graduate student). There was only one statistically significant relationship finding: recommending AI was dependent upon exposure to AI in the curriculum for career readiness. The chi-square test for independence was 7.150*. No significance was found in the subgroups of major/non-major status or year. These results are summarized in Table 5.

Table 5

Students feel that AI integration in courses enhances their readiness for their careers
(Survey Questions 10 and 11)

Survey question 11	Would recommend a Major	Would recommend by year	Would recommend AI in the curriculum
	χ2 =.349, p=.840	χ2 =.940, p=.322	χ2=7.150, p=.028
P<.05, *P<.01

Conclusion

Findings

The study expands on the current literature on using AI in accounting education. Specifically, the study focuses on a critical but often overlooked stakeholder perspective, the students.

Perceived Value of AI in Accounting Education

Using the survey of 119 participants across various accounting courses, we observed a consistent recognition of the importance of AI skills in their future careers, which was irrespective of the year of study or whether they were an accounting major or a non-accounting major.

Students identified data analytics, problem-solving, and technical proficiency as the three top skills that benefited from the integration of AI in the classroom and their future careers. A series of interrelationships among these skills were identified via a series of Spearman’s Rho tests, in which the students highlighted how creativity/innovation rankings were negatively correlated (R= -.461**, p<.01) with students’ emphasis on data analysis and interpretation. Students who highly ranked data analysis tend to rank creativity slightly lower, which suggests that students may see technical and creative proficiency as separate skill sets. While the year of study nor their major (accounting or non-accounting major) produced a statistically significant difference in the mean Likert-scale scores about the perceived AI value, the finding implies the AI exposure may be sufficient to align their perception of the career relevance of using AI across other disciplines and class standing

Perceived Challenges of AI Integration

The use of AI in the classroom by students notwithstanding the identification of challenges for effective AI integration. The students’ highest-ranked hurdles were limited access to real-world AI applications, insufficient hands-on practice, and concerns about instructor expertise. In the students’ identification of the most pressing barriers, survey participants often linked the lack of real-world application examples with insufficient course content (R=-.299**, p<.01), and a steep learning curve for AI concepts (R=-.332**, p<.01). In addition, inadequate instructor expertise also correlated with limited real-world application exposure (R=-.357**, p<.01), which can underscore how content and pedagogy can constrain learning. The data did not reveal statistically significant group differences by class or major, which suggests that the barriers of insufficient practice and resources are a constraint among all students, not just accounting students.

These findings add a dimension to the existing literature by quantitatively evaluating how students perceive and prioritize challenges. The analysis demonstrates that these challenges frequently interlink in students’ views (e.g., insufficient practice was strongly correlated with inadequate instructor knowledge, R=-.347**, p<.01). There was no evidence to suggest that any specific academic year or major felt more prepared. Collectively, this highlights a need for scaffolding AI integration in the curriculum from entry-level to advanced classes. It also suggests enhanced professional development for instructors. These collective highlights may be key to overcoming students’ perceived obstacles in AI-focused accounting curricula.

Impact on Career Readiness and Skill Development

The final theme examined whether the students perceived integrating AI in courses bolsters students’ career readiness. There were no statistically significant differences in self-reported career preparedness among the subgroups across majors, academic years, or prior AI exposure. However, 81.36% would recommend that more courses incorporate AI training to better prepare students for their careers.

There was a notable finding among students who had already been exposed to AI in their coursework. Those students were significantly more likely to advocate for the inclusion of AI for preparedness in their careers. No statistically significant relationship emerged by major or year of study.

Together, these results indicate that while most students value AI in preparing them for career readiness, the students who had been previously exposed to AI within a course appear to endorse the value of the use of AI in preparing them for career readiness. The direct exposure of AI in multiple courses leads to a higher level of AI for the student.

Filling a Gap in Literature

Previous research on using AI and Higher Ed often focuses on faculty perspective, pedagogical frameworks, and institutional constraints; however, they frequently do not capture the nuances of the student population. Previous studies have confirmed that with the use of AI, students can strengthen analytical and technical competencies. However, there is a gap in the evaluation of how students perceive the importance of those skills and the development of those skills to prepare them for career readiness. The findings of our research address the gaps by focusing on actual students’ experiences.

First, the research has determined that a key factor determining a student’s recommended AI training and use with other students is that they need to be exposed to previous coursework using AI. While previous research has described technology in the classroom as a broad term, our research reveals a specific, statistically significant link between student advocacy using AI and their AI course-related experience. Our research goes beyond mirror exposure in the classroom, which quantifies a student’s heightened endorsement of AI usage to even minimum AI content. This provides greater empirical evidence regarding specific curricular components that effectively shape perceived student preparedness.

Second, this research shed light on two areas of identified barriers to AI adoption and higher education of structural barriers and resource-based barriers. Previous research has acknowledged the importance of institutional infrastructure and faculty training. However, research has not evaluated the students’ perspectives on these barriers and specifically on how those barriers interact. The research analyses show a correlation that there is an interlink between the lack of real-world applications and inadequate instructor experience with using AI. The finding of this interlink fills a gap and research of understanding the different forms of support related to AI integration in the classroom.

Finally, previous studies have focused on advanced majors and/or single cohorts. This research provided comparative data at the crust academic level and both accounting and non-accounting majors that offer a larger view of how AI may potentially evolve across different learner profiles and time. This multi-level analysis indicates the enriching scaffolding of AI education integration at various points within a program and aligns with future investigations that may focus on specific student subgroups.

This research contribution addresses several pronounced gaps in the accounting education literature. This research also provides actionable insights that guide educators and administration toward strategies for AI integration in the curriculum design that is evidence-based.

Future Research Directions

To expand on this research and the gaps identified in the literature, there are multiple areas for building on these expanded findings and the identified gaps in the literature, via several avenues for future research.

Longitudinal Studies Across Course Progressions

This study captured cross-sectional perceptions at various academic levels and by accounting majors and non-accounting majors in an introductory accounting course; a longitudinal study design would illuminate how these student perceptions evolve as AI is integrated over time. Even tracking a cohort of students over multiple semesters would identify how learning engagement with AI shapes their identification of AI, related to the perceived value of AI in accounting education, perceived challenges of AI integration, and the impact on career readiness and skill development.

Expansion of Survey Participants

Replicating the study to a broader survey group based on other degrees or within a specific school, such as a school of business, would add to the generalizability of the research findings. By having a broader survey group, institutions would be able to evaluate AI integration of core curriculum courses beyond a specific major.

Diverse Institutional Contexts

Replicating this study across universities with differing profiles, such as large research institutions and community colleges, would further evaluate the generalizability of the findings. Institutional differences may influence the students’ perceptions of AI because of the availability of AI resources and faculty expertise in diverse ways.

Qualitative Explorations of Challenges

Qualitative research analysis would add more depth to the challenges the students ranked in this research. Such research could identify more nuanced information from student interviews and/or focus groups to identify the potential of how the barriers and challenges could be addressed more effectively from the student’s perspective.

Experimental or Intervention-Based Research

Future studies could use experimental designs to test the impact of AI learning and faculty development programs on students’ perceptions. For example, comparing cohorts that receive specialized AI assignments (i.e., real-world AI case studies, tutorials, or immersive partnerships with industry could yield robust evidence of integrating AI into the curriculum. The creation of hands-on, real-world case studies for the classroom holds significant promise. These case studies would allow students to apply AI tools to realistic scenarios—analyzing genuine or simulated financial data, detecting fraud patterns, and generating predictive models. By engaging students in problem-solving tasks, educators can measure the development of skill sets in accounting AI-driven work.

The direction of potential future research can potentially enable an actionable objective around AI integration in an educational curriculum, specifically an accounting curriculum. This would enable a nuanced insight into the role of higher educational institutions and educators toward shaping accounting curricula for the professional development of students to prepare them for a career that is increasingly using AI.

Research Limitations

Despite the valuable insights from this research into the students’ perceptions of AI integration in accounting education, this study has several limitations that should be acknowledged. The data were collected from one private, four-year institution in the state of Ohio in the United States. Consequently, the findings may not represent students at larger universities and community colleges.

In addition, it may not represent students in different geographic or cultural settings. The survey did have limits to the statistical power of 119 students when divided into major, academic year, and exposure to AI coursework. This may have constrained the generalizability of potentially meaningful differences across subgroups. The survey was also inherently susceptible to students’ self-reported data biases such as overestimation or underestimation on the rankings. The survey was also conducted at a single point in time. Without a longitudinal approach, it is difficult to determine if a student’s perceptions change significantly as they proceed through their curriculum or even in the real world.

The study did not assess broader institutional or classroom factors, such as faculty professional development efforts, budgets for AI resources, and/or partnerships with industries. Expanding on these limitations in future work would provide a more comprehensive understanding of how to effectively integrate AI into accounting education, which would better prepare students for the evolving demands of using AI in the accounting profession.

It is not an “either-or” proposition, however. The revised Bloom taxonomy is well-suited for some purposes. Curriculum design specialists, or faculty in charge of program review, might use revised Bloom to guide the assessment and revision of the entire, multi-year program (Feliberty & Rodriguez, 2022; Ching & de Silva, 2017; Karanja & Malone, 2021). Accreditation standards incorporate language from Bloom’s taxonomy (ACBSP, 2023; AACSB, 2020), so those who communicate with accreditation bodies should be conversant with Bloom. However, for typical instructors who want to prepare students to deal with complex, evolving aspects of professional life after college, the SLT is an excellent
place to start.

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