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Accounting profs. adapt to AI amid cheating concerns, other challenges

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The rise of generative AI in society has also given rise to AI-guided cheating in schools, a problem that has challenged educators’ capacity to adapt. While this issue is primarily associated with the humanities, accounting educators report that they are seeing this in their own classrooms as well. 

Generative AI is known not for its skill with numbers but words, which makes it an unfortunately ideal cheating tool for humanities courses that use written essays as major components of their programs. However, while accounting is not exactly 19th century romantic literature, language and writing are not entirely irrelevant. An accounting student may not need to analyze the major themes in Ulysses, but they may be called upon to interpret an accounting standard, tax regulation or audit document, which can be just as dense and confusing. So while there are not as many opportunities for AI-guided cheating as in other fields, students are still finding places where bots can do their work for them, much to the chagrin of their professors. 

“This is definitely something I have heard quite a bit about from my colleagues in the humanities and other fields, but is becoming an issue for accounting/finance classes as well. Students still need to understand the implications of ASU’s, disclosures, etc, and if they rely entirely on AI for assignment completion that knowledge will fade away,” said Sean Stein Smith, a professor at Lehman College who teaches intermediate accounting, cost accounting, advanced accounting and forensic accounting. He also leads Lehman’s development of AI business courses, as well its crypto/blockchain content.

AI cheating
Kyoto city, Japan – May 05, 2023: The OPEN AI logo visible on a smartphone screen

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He added that he has seen AI-guided cheating first-hand, especially for short-form essay assignments as well as when he requires students to perform financial analyses using specific ratios. 

Douglas Carmichael, former chief auditor of the PCAOB and currently a professor at Baruch College where he teaches auditing, noted that while he does not himself give any writing assignments that a student could use generative AI to cheat on, this doesn’t mean they’re still not using AI to undermine the purpose of an assignment, though once students realized he was on to them it has become less of an issue. 

“I do ask students to submit at least one question before class on something in the text or recorded lectures they found difficult to understand or want additional information about. My experience in prior semesters was that about half of the students submitted a question that seemed suspicious to me given the language used and generality of the issue. The lack of specific reference to the topic in the text or recorded lecture was also apparent. These kinds of questions did not earn any credit and as word got out about that use of ChatGPT is infrequent,” he said. 

But even if students are not out and out cheating, some have observed an unhealthy reliance on generative AI starting to form. Jack Castonguay, vice president of learning and development with Surgent as well as a Hofstra University professor who teaches advanced courses in accounting and auditing theory, has seen students struggling with understanding and communicating core concepts at least in part due to their reliance on generative AI. 

“We see the reliance significantly when they have to give a presentation or take an in-person exam. It’s clear they have gotten to that point by using AI and can’t apply the logic on their own. Maybe in 3-10 years (given the speed of the improvement in LLMs) they won’t have to do it on their own, but it’s a large problem now for client relationships and having conversations with this in practice. They need to look up everything and use AI as a crutch. Seminar discussions are like pulling teeth oftentimes for me,” he said. 

With this in mind, accounting educators — much like those in other fields — are currently in conversation about how to respond to this issue. Richard C. Jones, a Hofstra University accounting professor and former technical staff member at the Financial Accounting Standards Board, said this is a major topic of debate and discussion among college faculty and administrators, noting that it seems to be brought up in nearly every meeting. It is obvious, he said, that students will use LLMs on assignments, and so therefore the challenge for faculty is to assign projects and papers that require students to actually demonstrate their knowledge versus just handing in a paper or presentation. 

“Fortunately, I teach classes that require the application of accounting rather than accounting theory. Therefore, my exams and other assessments are specific to case information provided and application of the accounting rules in providing the journal entries and the related disclosure information. So, my students do not have as much of an opportunity to use LLMs to answer the questions,” he said. 

Additionally, he mentioned that educators are trying to find ways to work AI into their assignments, considering how quickly accounting firms themselves have taken to it. 

Tracey Niemotko — a Marist University professor who teaches accounting and auditing as well as sustainability, taxation and forensic accounting — said that she views AI as more of a tool than a cheating mechanism, pointing out how models can be used to expedite audit procedures or clear away the busy work that eats up the day of many professionals. Consequently, she is a little more sanguine about AI-guided cheating, noting that even if students do use AI in their assignments, the nature of the work makes cheating difficult. 

“Even with electronic testing in the classroom, I do not see cheating as a concern overall. I think the accounting students are perhaps a bit more disciplined than most students, so I don’t think they have the mindset to cheat. Even for writing assignments in my upper-level accounting courses, students may use AI to assist them, but they are required to write ‘in their own words.’ Overall, the majority do their own written work but may use AI as a tool to help them develop an outline or get them started,” she said. 

Abigail Zhang Parker, a University of Texas at San Antonio professor whose research specialty is AI in accounting, has also directly worked AI into her classes. For example, her Accounting Information Systems courses include hands-on workshops where students learn to operate different accounting software solutions. She noted that AI can be a useful tool for finding relevant information and understanding difficult concepts.

Therefore, her overall philosophy is that students can use generative AI to help with assignments but not on exams, as that is when they’re tested on their actual understanding of the topic. So long as it is only used for assignments versus exams, she does not consider using AI to be cheating. She added it would be impractical to prevent the use of AI entirely anyway, it’s better for educators to find ways to use it too. However, she noted that teaching students proper use of AI can, itself, present a challenge. 

“Perhaps we need to guide them how to use it properly. This is not easy. One method that came to my mind is to make the parts that demonstrate students’ own skills take a greater portion in the grading components.  … For example, there are three exams throughout the semester, and they take 60% of the total grade, while assignments take 10%. For classes where students need to submit a report and make a presentation, maybe the report itself will not take up a high portion of the grade, but the in-person presentation will, as it better reflects students’ true understanding of the subject. And once students know that they will be mainly graded on their own performance, they are more incentivized to think through the problem than simply over-relying on AI,” she said.

Another reason to learn AI in the classroom is that, once students are working as professional accountants, clients will likely be using AI as well, and they will need to understand and explain what is missing from the AI’s answers. However, Castonguay, from Hofstra, voiced concerns that over-reliance on AI is eroding the critical thinking and reasoning skills needed to properly evaluate these answers in the first place. He does an exercise in class where students have ChatGPT summarize a FASB ASU and review its findings. Some, he said, don’t even know where to start as they have obviously been relying on ChatGPT to understand it at all. 

“My bigger concern is [that] by such a reliance on AI they will lack the critical thinking and synthesizing skills that are still valued even with AI. To use a sports analogy, they are only bowling with gutter guards – what happens when those aren’t there?” he said. 

Smith, from Lehman, said these kinds of things underscores the need to teach responsible AI usage in a way that does not degrade the human skills that they’ll be relying on in the professional world. He felt, unfortunately, that this could be an uphill battle. 

“I do think that as AI becomes more integrated into the classroom and profession, we are going to have to really double-down on making sure students still have the ability to think critically. Especially in cases where questions or data may change on-the-fly, students are seeming to have a harder time pivoting and adapting to analyze said data on the spot. It’s a growing problem with no cookie-cutter or easy solution, but is definitely something I know is being talked about in pretty much every accounting department/School of Business,” he said. 

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FASB Standardizes Carbon Offsets Accounting Rules

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FASB Standardizes Carbon Offsets Accounting Rules

In a decisive move toward standardized environmental financial reporting, accounting standards boards issued updated implementation guidance during the week ending July 25, 2026, regarding the formal recognition and valuation of corporate carbon offsets and environmental credits. The revised frameworks establish precise rules for how enterprises must measure, record, and disclose carbon credits on balance sheets, eliminating years of inconsistent reporting practices across public capital markets.

Under the finalized accounting standard, purchased carbon offsets can no longer be categorized under vague administrative expenses or unstandardized intangible asset accounts. Instead, organizations must classify environmental credits based on underlying operational intent—distinguishing between credits held for immediate compliance compliance obligations, long-term offset obligations, or active market trading. Furthermore, companies are required to evaluate carbon holdings for fair value impairment at the end of each reporting period, ensuring that depreciated or low-quality environmental credits do not distort corporate asset values.

The standardized rules carry significant implications for corporate audit committees and chief accounting officers. External audit firms are implementing rigorous verification protocols to validate the physical legitimacy, legal ownership, and scientific permanence of carbon credits claimed on balance sheets. Inaccurate or overstated carbon accounting claims now carry substantial financial litigation risk, alongside potential regulatory enforcement for misleading ESG disclosures.

To remain fully compliant, corporate accounting departments must establish centralized carbon tracking systems integrated into primary standard ERP ledgers. Accounting teams that proactively adopt standardized environmental reporting protocols will build investor credibility, streamline annual audit processes, and insulate their organizations against evolving regulatory scrutiny.

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Accounting

Automated Tax Compliance Tools Reduce Risk

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Automated Tax Compliance Tools Reduce Risk

Corporate tax departments reached a critical juncture in automated operational management. With nations worldwide rapidly enacting digital service taxes, localized value-added tax (VAT) mandates, and real-time electronic invoicing requirements, manual tax calculations have become obsolete. Modern corporate tax divisions are aggressively deploying AI-driven tax engine software to automate complex cross-border indirect tax calculations in real time.

The imperative for automated tax compliance stems from the sheer complexity of current trade policies and multi-jurisdictional commerce. E-commerce platforms, software vendors, and global manufacturers face constantly changing regional tax rates, statutory exemption rules, and cross-border tariff structures. Automated tax engines embed directly into enterprise enterprise resource planning (ERP) architectures, automatically applying correct tax codes at the point of sale, calculating real-time withholding amounts, and generating compliant e-invoices.

Automated audit trail generation represents another key advantage of modern tax tech integration. Advanced compliance platforms log every transactional tax determination on immutable digital ledgers, providing tax authorities with transparent, self-verifying audit trails. This capability drastically reduces the operational duration and administrative cost of corporate tax audits, protecting enterprises against severe penalties resulting from calculation errors or missed reporting deadlines.

For chief financial officers and tax directors, investing in automated tax compliance is a vital operational risk mitigation strategy. Automating routine tax calculations frees high-level accounting professionals to focus on strategic tax planning, transfer pricing optimization, and risk management in an increasingly complex global economic environment.

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Accounting

Continuous Auditing Transforms Corporate ERPs

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continuous auditing transforms corporate erps

As corporate accounting departments cross the threshold into late July 2026, the adoption of continuous, automated auditing systems has reached a definitive turning point. Driven by advances in artificial intelligence and deep integration with modern Enterprise Resource Planning (ERP) platforms, leading finance organizations are moving away from traditional, periodic post-hoc audits in favor of real-time, 100% transactional verification. This technological transition is redefining internal control environments, reducing compliance costs, and eliminating the structural delays inherent in legacy quarterly closing processes.

Unlike traditional auditing frameworks that rely on statistical sampling—a process that inevitably leaves operational blind spots—continuous auditing software monitors operational data feeds continuously. Every purchase order, electronic invoice, payroll disbursement, and cross-border wire transfer is automatically cross-referenced against established corporate governance parameters, regulatory tax schedules, and anti-fraud algorithms in real time. Anomalies or unauthorized ledger entries are flagged instantly, allowing internal audit teams to investigate and remediate compliance gaps immediately rather than months after the close of a financial period.

The implications for executive financial management are far-reaching. By embedding continuous verification directly into daily transaction workflows, chief financial officers gain uninterrupted visibility into the organization’s true financial standing. Real-time balance sheet auditing eliminates the severe operational bottlenecks associated with month-end and quarter-end financial reconciliations, freeing accounting professionals to focus on strategic financial modeling, tax planning, and capital allocation rather than manual data entry and spreadsheet consolidation.

However, implementing continuous auditing requires accounting leadership to invest heavily in data governance and technical upskilling. Internal audit teams must evolve from manual ledger reviewers into system architects capable of auditing complex algorithms and validating automated data pipelines. Accounting firms and corporate controllers that master continuous auditing will establish a resilient compliance framework capable of meeting stringent international regulatory standards with total transparency.

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