AI is here to stay in Personal Injury and Disability Management

Artificial intelligence (AI) tools are everywhere, and they are not going away.

If you are a personal injury and disability management (PIDM) professional, leader, learner or educator, AI tools are part of your reality. That does not mean we are acting on (or reacting to) this reality in the most appropriate way.

Over the last two years, I have spoken about AI with boards of directors, disability management professionals, rehabilitation practitioners, and educators across workers’ compensation and disability insurance systems. Audiences recognized the potential benefits of AI in this domain:

• Faster identification of cases requiring intervention
• Earlier referral to supports and services
• More individualized treatment and recovery pathways
• Improved RTW planning and coordination
• Reduced administrative burden on practitioners
• More time available for direct client engagement

Central to PIDM professionalism is the health and wellbeing of the client while maintaining ethical standards. AI tools can help achieve that objective.

It is incumbent on professionals to be current keep current with the body of knowledge underpinning PIDM. Here, again, AI tools can help identify and apply the ever-evolving knowledge base to the benefit of client outcomes.

Professionals in all fields are accountable for the tools they select and use. PIDM professionals confirm the benefits of AI tools but repeatedly raise three issues:   

1.     Organizations not articulating a clear vision for AI.

2.     Inadequate or inconsistent professionally-specific training on AI tools.

3.     Intensifying cognitive load, leading to AI “Brain Fry.”

Organizational Direction

The concern most reported by PIDM professionals is not rooted in the AI tools themselves. It stems from the organization: leadership uncertainty, vague expectations, and unclear boundaries around how AI should be integrated into daily practice.

Many professionals report receiving communications focused primarily on the risks of AI. Some describe policies that prohibit AI use outright. Others report blocked access to AI platforms or disabled AI functions within workplace software.

At the same time, many PIDM practitioners quietly acknowledge using personal AI accounts to assist with tasks central to their professional responsibilities and work demands.

This creates an uncomfortable situation for both organizations and employees.

From an organizational perspective, unauthorized use can create confidentiality, privacy, and security risks. From the practitioner's perspective, however, the technology may represent a legitimate opportunity to improve service quality and efficiency.

As one PIDM professional asked:

"How can I provide the best service to injured and disabled clients with one hand tied behind my back?"

Others report observing peers who are achieving better results through effective, although unauthorized and undisclosed, AI use.

One vocational rehabilitation specialist summarized the concern this way:

"I'm worried I'm falling behind. If I want to advance in this profession, I need to keep up with AI technologies."

Organizations that focus exclusively on restriction may inadvertently encourage shadow AI practices while missing opportunities to establish safe, effective, and transparent approaches to adoption.

Inadequate Training

Professional responsibility requires practitioners understand the tools they use—including AI technologies.

Where organizations have authorized the use of AI tools (such as ChatGPT, Microsoft Copilot, Gemini, or Claude), PIDM-specific training frequently lags deployment.

Following a recent conference keynote presentation, a cluster of PIDM professionals highlighted their reality. Their organizations had provided AI technologies but with limited and generic instruction. The training focused on common administrative tasks with little guidance or “use case” examples related to disability management, claims administration, rehabilitation, or return-to-work coordination.

As one case manager explained:

"We were given the AI tools, but we were expected to figure out the specifics of using them ourselves."

In one large agency, PIDM professionals reported creating informal learning networks, sharing successful prompts, use cases, and lessons learned with or from other colleagues.

While this initiative is encouraging, organizations should not rely on informal improvisation alone. Effective AI implementation requires role-specific training, governance, and ongoing professional development.

Just as organizations invest in training for case management systems, clinical protocols, and legislative requirements, they must also invest in both AI literacy and specific AI applications they authorize and provide.

AI "Brain Fry"

One of AI's greatest advantages is its ability to automate routine tasks.

Whether through generative AI tools such as ChatGPT, Copilot, Gemini, and Claude, or emerging agentic AI systems capable of carrying out more complex workflows, organizations are increasingly using AI to reduce administrative effort and accelerate decision-making.

Many practitioners appreciate these efficiencies. As one therapist noted:

"It speeds up documentation and correspondence, allowing more time with my clients."

Yet the same therapist added:

"I'm glad to have access to AI tools, but I'm feeling more exhausted than ever."

This observation points to an emerging challenge a client service representative described succinctly:

"It's not that I am dealing with more cases. The cases I am dealing with are just much more complex."

A board member from a workers' compensation authority captured what may be occurring:

"AI removes the simple work… it changes the mix… accelerates the complex work, and leaves [PIDM] professionals with a higher volume of cognitively demanding decisions."

She succinctly christened this phenomenon as the AI intensity effect and related it to a similar observation in nursing where the number of beds in a ward are not increasing but the healthcare needs and nursing demands are.

As AI assumes routine administrative activities, practitioners may find themselves spending a greater proportion of their workday addressing complex exceptions, nuanced judgment calls, difficult client interactions, and high-consequence decisions. The total caseload may not increase, but the cognitive demands of that caseload does.

Recent research reported in the Harvard Business Review refers to this experience as "Brain Fry"—a state of mental exhaustion and cognitive strain associated with intensive AI use. While the article highlights the substantial benefits of AI, it also notes that productivity gains may diminish and cognitive burden increase when employees are required to manage multiple AI systems simultaneously. [See Julie Bedard, Matthew Kropp, Megan Hsu, Olivia T. Karaman, Jason Hawes and Gabriella Rosen Kellerman, When Using AI Leads to “Brain Fry,” Harvard Business Review, March 5, 2026, available at https://hbr.org/2026/03/when-using-ai-leads-to-brain-fry].

For PIDM professionals, whose work already involves complex decision-making, empathy, ethical judgment, and interpersonal communication, this risk deserves serious attention.

Technology Alone Is Not an AI Strategy

AI implementation is not simply a technology project. It is a workforce transformation initiative.

Boards of directors, executives, and professional leaders should be asking:

• Do we have a clear AI strategy?
• Are we providing role-specific AI training?
• Do employees understand appropriate and inappropriate uses of AI?
• Are we monitoring how workload intensity changes as AI tools are introduced?
• Are we preparing managers and staff to recognize signs of AI-related cognitive strain and fatigue?

Employers have increasingly recognized what PIDM professionals already know: “psychological health and safety” is an essential component of workplace risk management. As AI becomes more deeply integrated into professional work, leaders must consider the risk of AI-related workload intensity and cognitive strain; the must recognize this emerging psychosocial hazard and take steps to manage that risk.

The irony is difficult to ignore. Many organizations are implementing AI to improve productivity, enhance service delivery, and reduce administrative burden. Yet if AI deployment results in sustained mental fatigue, cognitive overload, or increased psychological stress among employees, those gains may be offset by reduced engagement, burnout, turnover, or even psychological injury.

For organizations whose mission includes preventing injury and supporting recovery, this issue deserves particular attention. Workers' compensation authorities, disability insurers, and rehabilitation providers have long understood that workplace systems and job design can significantly influence health outcomes. The same principle should apply to AI implementation.

The business case for AI extends beyond productivity gains and cost savings.

In workers' compensation, disability insurance, and rehabilitation systems, the ultimate objective is better outcomes for the people we serve. If AI can help identify needs earlier, support better decisions, improve treatment pathways, and facilitate safer and more durable return-to-work outcomes, then its value is significant.

However, achieving those outcomes requires investing in people as deliberately as we invest in technology.

The future of AI in PIDM will not be determined solely by the sophistication of the tools. It will be determined by how effectively organizations help professionals use those tools while preserving the human judgment, expertise, and wellbeing that remain at the heart of the profession.

Organizations that successfully navigate the AI transition will be those that view AI not merely as a technology initiative, but as a strategic change effort that balances innovation, professional competence, psychological health and safety, and client outcomes. In that respect, preventing AI "Brain Fry" may prove to be just as important as implementing AI itself

Where does AI fit in PIDM professional training?

How do we prepare the next generation of PIDM professionals for this reality? I argue against a passive, permissive approach. Our students need to be prepared to use AI tools in the workplace. Intentional instruction on AI must be part of the curriculum. Those entering professional roles need to know how AI tools work. They also need to recognize improper use of AI in the workplace.

For the last few years, I provides guidance on permitted and unacceptable uses of AI in the courses I teach. I required students disclose the AI tool use in an “AI Attestation” statement submitted as part of formal assignments. This allows students to confirm their knowledge of AI tools and their intentional use of AI in assignments. It also provides an opportunity for dialogue on proper uses.

Incorporating AI literacy into the curriculum is just one element of what educators must consider. Educational assessment needs to be AI sensitive. Assessing mastery of PIDM skills needs to be AI-aware. Failing to prepare future professionals for the reality of AI in their roles may limit their career entry and progression. I certainly want the student I teach to be AI literate and sensitive to both proper and improper use in the profession.

Failing to provide AI literacy and appropriate training may leave the next generation of PIDM professionals and those they serve open to harm. 

The AI Challenge for PI and DM professionals

If you are a professional in personal injury and disability management (PI/DM), return-to-work (RTW) coordination or related fields, Artificial Intelligence (AI) brings new challenges. The advantages of using AI are clear: new insights, expedited workflow management, and improved communication leading to better outcomes for clients.

Optimizing client outcomes is fundamental to the profession. Failing to use AI tools—or failing to use them appropriately—run counter to the profession’s objectives.

AI Literacy and Use in PI/DM

In speaking on AI and PI/DM for the last two years, I have encountered apprehension among professionals in this field. At a recent Canadian Society of Professionals in Disability Management (CSDPM) event, I asked the audience to respond to four questions. The live “vote” (n=254) results are revealing:

• Do you use the internet most days? 98% yes
• Do you consciously interact with artificial intelligence (AI) most days? 48% yes
• Does your organization have a well understood policy or guideline for the use of AI? 38%  yes
•  Are you using AI in your professional DM work? 30% yes

While nearly universal daily internet use, less than half report consciously interacting with AI and less than a third are using (or admitting use) of AI in their professional work.

A lack of AI literacy may account for this low level of engagement.

AI Literacy, Transparency, and Use

AI literacy refers to the ability to critically understand, responsibly use, and effectively evaluate artificial intelligence tools and systems. This is not just technical knowledge; AI literacy requires ethical awareness, contextual judgment, and transparent communication about AI’s role in shaping information, decision-making, and practice.

Transparency is important. From their formal training, most PI/DM professionals are aware of academic integrity rules particularly around plagiarism, but AI use requires a distinct set of rules.

As an instructor, I allow the use of AI tools in my courses and require an AI Attestation with acknowledging the use and extent (if any) of AI tools in formal assignment submissions.   I do not encounter many examples of plagiarism, but I am seeing what I call “plagi-AI-ism,” the use of AI tools to generate or significantly shape content, without proper acknowledgment of the AI’s role.  This lack of transparency runs counter to academic integrity and professionalism.

To be clear, the appropriate and transparent use of AI tools — such as language refinement, citation help, or idea generation — is encouraged and allowed if properly disclosed. Plagi-AI-ism refers only to non-transparent, misleading use of AI that obscures authorship, intellectual effort or critical professional considerations.

A recent KPMG/University of Melbourne study (Gillespie, N., Lockey, S., Ward, T., Macdade, A., & Hassed, G. (2025). Trust, attitudes, and use of artificial intelligence: A global study 2025. The University of Melbourne and KPMG. DOI 10.26188/28822919) found Australia, Canada, and New Zealand have among the lowest levels of training, literacy, and trust in artificial intelligence systems in the world.

AI education and training is reaching about 24% of the population in these countries, lagging slightly behind the US (28%) and well behind China and India (64%), Singapore and Switzerland (45%), and Denmark and Italy (34%) as well as dozens of other countries.

AI adoption in the workplace

The use of AI at work is growing. The KPMG/UofM study notes:

“Three in five (58%) employees intentionally use AI at work on a regular basis, with a third using it weekly. Generative AI tools are most used with many employees opting for free, publicly available tools rather than employer-provided options. Emerging economies are leading in employee adoption with 72% using AI regularly compared to 49% in advanced economies.”

AI use by knowledge workers (including professionals in PIDM, RTW, HR) may well be significantly higher. A study by Microsoft and LinkedIn (Microsoft and LinkedIn, 2024 Work Trend Index Annual Report, May 8, 2024, https://news.microsoft.com/2024/05/08/microsoft-and-linkedin-release-the-2024-work-trend-index-on-the-state-of-ai-at-work/) found:

• •       75% of global knowledge workers use AI at Work—and won’t wait for companies to catch up.
• •       78% of AI users are bringing their own AI tools to work (BYOAI)—it’s even more common at small and medium-sized companies (80%).
• •       52% of people who use AI at work are reluctant to admit to using it for their most important tasks.

AI is everywhere.

Part of the lower-than-expected results from my recent survey may be due to a lack of awareness about the prevalence of AI technologies in the tools used by professional in DM and related fields. To be clear, AI is everywhere professionals; consciously or not, PI/DM professionals are interacting with AI. Ignoring it or banning its use on the job is not an option.

If you use the internet most days—as two-thirds of humanity do—you are interacting with AI. AI is built into most office software.  Search engine results are mediated by AI.  Every time you complete a CAPTCHA, you are advancing AI.  When you hear the call centre message, “Your call may be monitored for training purposes,” there is a good chance your interaction is helping train AI.

AI is pervasive and evolving.  PI/DM professionals must keep pace. AI literacy is central to this task.

Setting rules around AI

Universities have rules about the use of AI and academic integrity is an imperative; yet the KPMG/UofM study found:

• Most students have used AI inappropriately, contravening rules and guidelines and over-relying on AI.
• Two-thirds have not been transparent in their AI use, presenting AI-generated content as their own and hiding their use of AI tools.
• Only half regularly engage critically with AI tools and their output.

While 38% of respondent in my survey said their organization had a well understood policy or guideline for the use of AI, the well understood policy was often a blanket “Don’t use AI.”  For these professionals, many AI chatbots are off limits and certain functions on corporate workstations have administrator restrictions.

Despite controls and policies like this, the KPMG/UoM study found “a third (35%) of employees report that the use of AI tools has resulted in increased compliance and privacy risks, such as contravening rules, policies and local laws.” 

AI enabled apps such as Microsoft 365 with CoPilot are typically approved for PI/DM professionals in corporate settings. In probing their actual use, respondents (often) reluctantly confirm the Microsoft/LinkedIn survey result:  DM professionals are using personal devices to augment approved use.

This is level of use is similar in other professional settings. The Queensland University of Technology study (McDonald, P., Hay, S., Cathcart, A. & Feldman, A. (2024). Apostles, Agnostics and Atheists: Engagement with Generative AI by Australian University Staff. Brisbane: QUT Centre for Decent Work and Industry. https://eprints.qut.edu.au/252079) found 71% of university staff respondents used generative AI for their university work.

• Academic staff (75%)
• Professional staff (69%)
• Sessional staff (62%)
• Senior staff (81%)

 The Growth of In-house AI Solution

Many insurers, agencies, rehabilitation providers are now developing their own AI solutions. The rationale for such investments includes the primacy of client confidentiality, competitive advantage, improvements in efficiency, and better outcomes for clients.

In conversation with three senior executives working on their own AI solutions, the obstacles are clear. The time, training, and human resource allocation efforts are immense, and the payoffs are often incremental, at least initially, compared to the cost and effort.

Liberty Mutual reports productivity gains of 1.5 hours per week for the 25% of employees now using their LibertyGPT system. Manulife notes the gains in fraud detection, underwriting, and claims support. Several firms are using AI to train new agents and call centre representatives. A few are now rolling out internally developed agentic AI systems to work with and, in some cases, replace humans doing more routine tasks.

Implications

Governance, board,  and C-suite leadership have a significant issue. Most organizations do not have a formal AI strategy. You need one. And do not expect it to be easy to develop, cheap to implement, or finite in terms of consideration. This is not a “one and done” agenda item.

Administrators have a tough task ahead of them. Chances are many of your professionals are already using AI, skirting any formal policy you may have and pushing the boundaries where policy or guidelines are lacking. You have regulatory imperatives and may be facing a talent crush amid other priorities including budgetary constraints.

PI/DM professionals have enduring professional priorities including.

• The health, well-being, and best interests of the client
• Maintaining ethical standards and accountability
• Continuing Mastery of DM’s evolving knowledge base
• Adapting proactively to changes in environment and evolving technology landscape.

AI technologies are at the nexus of these professional responsibilities. Increasing AI literacy is fundamental to meeting professionalism.

Improving AI Literacy levels

The KPMG/UofM study highlights this fundamental relationship:

“AI literacy is lagging AI adoption yet is critical for responsible and effective use…AI literacy. is associated with greater use, trust, acceptance, and critical engagement, and more realized benefits from AI use including more performance benefits in the workplace.”

In PI/DM, performance benefits in the workplace translate directly to better service deliver and outcomes to clients.

That begs the question:  how does a professional in personal injury and disability management improve AI literacy?

The QuT study asked AI-user respondents what resources they used to learn about AI tools, or AI more generally. The results were revealing. Informal sources including peers, colleagues, family, or friends topped the list (61.3%); Google searches were the course for half the respondents and (46.6%), YouTube videos were a source for a third of respondents (32.1%). Responses listing formal sources for gaining AI literacy ranked much lower:  conferences or seminars (16.9%), workshops (13.2%) and blog posts like this one (12.5%). Only eight of the 2315 (0.4%) responses noted their degree or qualification curriculum as a resource.

If those results do not scare you, consider the misuse potential inherent in untrained use of AI tools. AI tools are not without limitations and risks. Professionals in all fields are responsible for selecting the right tool for the job, understanding the inherent risks associated with that tool, and having mastery of the tool before employing it in the execution of their work. Without formal training and integration of AI literacy into professional development and qualifications, there is the real risk of harm to clients.

Final thoughts for professionals

AI is here and not going away.  PI/DM professionals must evolve with AI technology and improving AI literacy is part of that. 

Learn about AI but not just from YouTube videos.  Engage in courses and sessions that provide formal, authoritative instruction.  Align your use of AI with the values of the profession.  Anticipate how others are using AI in the products or instructions you receive and be conscious of how others will use AI with the work you produce and services you provide.  Think critically.  AI does not replace your professional judgement.  You must be aware of the imitations and weaknesses of the tools you use.  And with any changing field, you need to stay informed. Where possible, be part of the processes setting standards and guidance for the use of AI in your profession.

To paraphrase a recent IBM study, AI won’t replace PI/DM professionals but PI/DM professional who use AI will replace those who don’t. [IBM Institute for Business Value | Research Insights, 2023, “Augmented work for an automated, AI-driven world”]

Artificial Intelligence and Real-world Implications in Disability Management

 

“Leveraging Artificial Intelligence (AI) in Personal Injury and Disability Management.”  I’ve delivered presentations on this topic at the International Forum on Disability Management (IFDM)  in Vancouver (BC), the Personal Injury Education Foundation (PIEF) Conference in Perth (Western Australia) and to audiences at online webinars and roundtables. While I could attend only a fraction of seventy-five or so sessions and hundred other presenters, what I’ve learned through my attendance and interaction with presenters, providers, and delegates (including injured workers) at these events has implications for everyone working in this field.  Here are a few highlights related to AI in personal injury and disability management.

AI is everywhere …even if we are reluctant to admit it

Microsoft quotes an International Data Corporation (IDC) study finding 70%+ of Fortune 500 companies have now use CoPilot in their organizations (Ignite 2024: Why nearly 70% of the Fortune 500 now use Microsoft 365 Copilot November 20, 2024,  Microsoft Hong Kong).

About 75% of knowledge workers worldwide are using AI in their work (Microsoft and LinkedIn, 2024 Work Trend Index Annual Report, May 8, 2024 https://news.microsoft.com/2024/05/08/microsoft-and-linkedin-release-the-2024-work-trend-index-on-the-state-of-ai-at-work). 

Use of AI by university staff ranges from 62% for sessional staff to 81% for senior staff with academic staff at 75%   (McDonald, P., Hay, S., Cathcart, A. & Feldman, A. (2024). Apostles, Agnostics and Atheists: Engagement with Generative AI by Australian University Staff. Brisbane: QUT Centre for Decent Work and Industry. https://eprints.qut.edu.au/252079)

What I learned in presenting this information is that AI use among professionals in personal injury and disability management is in its initial stages. This is not to say personal injury and disability management (PI&DM) professionals are unaware of AI’s potential. About a third of delegates I encountered work employers who have implemented enterprise-wide AI solution such as CoPilot or ChatGPT enterprise editions. What has been missing is the direct and specific information they need to effectively use AI tools for their specific tasks. Other delegates report overly restrictive prohibitions against using AI at work, although many admit to simply using AI on their own smartphones or laptops or on home computers. This last point is consistent with the Microsoft/LinkedIn study that found 52% of people who use AI at work are not waiting for their employers to catch up, are bringing using AI on their own devices, and are reluctant to admit to using it for their most important tasks.

AI is pervasive in the PI&DM research and provider community

Although some organizations are restraining or restricting application of AI, the research community is applying the technology to significant effect. One presentation at the IFDM demonstrated this effectively. “Using Ensemble Random Forest Algorithms to Predict and Determine Return to Work Intervention and Pathways” (Mohamad Amrizad Bin Ruslin & Nabilah Binti Ahmad, presentation at IFDM 2024, Vancouver) might not have the catchiest title but the results were exceptionally noteworthy.

Working for Malaysian Social Security Organization (SOCSO) – PERKESO, the research focused on supporting workers with disabilities through personalized Return to Work (RTW) interventions. The challenge is familiar to every workers’ compensation and disability insurance program administration: to optimize case management to reduce the duration and cost of rehabilitation while increasing success rates. The research employs a collection of AI tools to predict and recommend tailored RTW interventions based on individual worker data (e.g., injury type, job demands, rehabilitation needs) at or near the claim acceptance. The results demonstrated high predictive accuracy, increased efficiency in allocating resources, and faster return to work. The researchers also introduced their new  work on creating their own “official disability guide” based solely on Malaysian disability cases. This makes sense; while quantifying “impairment” is standards-based assessment, “disability” duration and impact are dependent on external factors including access to care, employment law,  and societal acceptance.

Use of AI among providers to the personal injury prevention, worker’ compensation, and disability insurance industries is advancing quickly. Advanced analytics such as those demonstrated by Clara Optics provide risk identification using AI’s assessment of unstructured data sources was a good example. Individual claim alerts display when there are changes in sentiment, pain, medication, or psychological impact.

Michelle Barratt for Arriba Rehab Management (RM) presented their propensity model (Lean-On_Learning_Assistance-LOLA). This up-front AI application accurately predicts worker risks, allowing RM to tailor service delivery pathways in line with evidence-based protocols, resulting in improved RTW outcomes, reduced case costs, and durations.

Amanda Johnston demonstrated the integration of AI into case KINNECT’s CareLever platform of claims management systems. The Dashboard  in their “Manage” module provides case-specific, real-time status of outcomes and quality, client centricity, and even file “hygiene.”  One interesting feature was the digitization of psychosocial questionnaire instruments such as the ARIBA (Assessment of Risk for Interpersonal Violence or Abuse), DAS (Depression Anxiety Stress Scales) and Fear Avoidance Belief Questionnaires (FABQ): The Fear-Avoidance Belief Questionnaire. This method avoids the delay between the time a case manager determines the need for such assessments and the return of scored results by allowing for delivery to the client by SMS messaging, automated scoring and return to the case manager.

Several presenters spoke about their implementations of AI include CoPilot. Presenters and  service providers in the exhibition space demonstrated the power of leveraging AI to create promotional and educational materials. For example, a complex regulatory-change news release transforms into podcast discussing the changes through a generative AI application; that’s a particularly powerful demonstration of making information more accessible for certain learning styles. One service provider now creates podcasts from curated RSS (really simple syndication) feeds for their clients.

Functional assessments are always a challenge in PI&DM. In countries such as Australia, where long distances may be involved, the application of XTRA’s AI application to perform virtual measurements such as range of motion from real-time video consultation sessions was impressive.

This power of AI to eliminate delays in the sequence of case management events was evident several presentations and product demonstrations I viewed at both the IFDM and PIEF conferences. PI&DM professionals can help clients achieve the best outcomes when diagnostics, assessments, and treatments occur without delay.

Compliance with prescriptions and medical instructions can also lessen duration of disability. Several presenters and providers demonstrated AI systems helping clients more actively participate in their treatment and rehabilitation through SMS messaging and notifications  to prompting medication use,  exercises, symptom reporting, and appointment attendance.

Dr. John McMahon (Navigator Group) presented his practical application using machine learning in PI&DM. One application was Jin, an avatar-based virtual claims examiner. While not perfect, the AI-driven Jin interacts with clients, collects data, and can do so in more than one hundred languages. The power and potential of this technology illustrates the breadth of where AI is taking us.

Implications for PI&DM

These recent conferences and interactions with audiences on AI illustrate the challenges and opportunities AI presents PI&DM. It is evident that there is an explosion of work in the AI field but AI literacy among professionals is lagging. A recent study found that more than 70% of university staff and instructors were using AI in their work, but gaining their knowledge about AI from friends, family, and YouTube videos. Informally, the students and PI&DM professionals I encounter typically have experimented with AI but lack fundamental understandings about this technology, its ethical uses, and limitations.

Leadership in PI&DM organizations must make AI strategies and guidelines a priority. They must also recognize that this is not a “one and done” exercise. Whatever you decide, you will have to revisit your strategy often.

Administrators have a tough task when it comes to AI. They must adapt to the changing environment, select applications and products, while maintaining privacy, confidentiality, and regulatory responsibilities. Even these tasks require raising AI literacy levels throughout the organization. They must recognize that restricting AI use is a futile effort. With 80% of  knowledge workers already using AI, enabling ethical, regulatorily compliant use is the only option. Gaining and sustaining professional levels of AI literacy needed to achieve that will be an ongoing challenge in terms of time, effort, and cost.

Individual practitioners need a professional level of AI literacy in order to critically assess the AI tools they use and to understand both the upstream use and downstream consequences of AI proliferation. 

For educators, have similar challenges professionally. Raising and maintaining our own AI literacy while incorporating AI into our courses to better prepare PI&DM professionals is an immediate priority.

Final thoughts

A 2023 IBM publication concluded “AI won’t replace people, but people who us AI will replace people who don’t.”  To put this more directly for our profession, AI will not replace PI&DM professionals, but PI&DM professionals who use AI will replace those who don’t.

There is no avoiding what AI is bringing. Only increased AI literacy among PI&DM professionals, administrators, leaders will optimize the impact of AI for the clients we serve.

Personal Injury & Disability Management Education in the AI Era

Artificial Intelligence(AI) is a reality. It is in our workplaces; it’s integrated into the software we are already using; and it is not going away.

Eighty percent of knowledge workers are already using AI (according to IBM/LinkedIn survey May 2024); everyone with access to the internet-that’s more than 2/3rds of the human population on earth—has access to AI tools. The standard desktop applications we use everyday —Google Workspace, Microsoft Office, Adobe Acrobat, Google Search— are already AI-enabled.

AI leverages scarce resources , empowers people with disabilities, and facilitates access to education and employment. This is the AI reality and it is just the beginning.

Generative AI in Continuing PI&DM Education

As a continuing education instructor in the personal injury and disability management (PI&DM) space, I’ve detected the use of AI tools in on-line discussion posts and formal assignment submissions over the last two years. That’s not really surprising given the universal access to many AI tools and the growing number of organizations with enterprise-level deployments of AI applications or platforms with inherent AI features. This only adds to the urgency for changing my instructional approach.

In my view, PI&DM educators must recognize this AI reality and prepare professionals to understand and use AI technology in both their work and studies.

That’s why I now allow AI tools to be used in the courses I teach.

AI in a PI&DM course: an example

In a recent on-line DM continuing educational course, I permitted (but did not require) the use of AI tools. The one week continuing education course is structured around two discussion exercises and formal assignments. In each exercise, each student selects a case scenario and provides an initial post of 250-400 words. Other participants engage through online discussions by offering posts of 150-250 words that probe issues, add alternatives, or link proposed actions to literature or experiences. The interaction mimics a live classroom discussion, in an asynchronous format.

A Discussion Grading Rubric provides greater marks for greater participation along with allocations for demonstration of knowledge and critical thinking as well as meeting timeliness/length requirements. The discussions are the students’ opportunity to demonstrate their knowledge of course content and ability to apply concepts covered in the course materials.

Prior to the start of the course, every student received an email explaining the permissible (but not required) uses of AI tools. The advisory was replicated in the Required Reading section of the on-line module. A required 8 minute video (summarized in the slides posted here) provided further guidance on acceptable and unacceptable uses of AI tools. It introduced some common issues with AI tools including hallucinations.

An additional 22 minute video on AI was offered in the optional resources section of the module. This video provided basic AI literacy information.

The email, required reading instruction, and required video contained the following instruction regarding the course assignment:

You must provide an “attestation” statement with your assignment submission declaring:

a.      The AI applications were used in preparation of your assignment submission and for what purposes, OR

b.      No AI applications were used in the preparation of this assignment submission. 

Please view the required video for more information [link to 8 minute video].

Results from Discussion Exercises

Seventeen participants were registered for the course. On average, there were about 5 posts on three of five discussion scenarios, about eighty posts in total.

Only two discussions posts contained indicators of inappropriate citation and reference use. In both cases, unverifiable references (likely "AI hallucinations”) were detected resulting in a zero grade for the exercise.

Citations and references support the posted arguments and intended to provide evidence of understanding of course content and appropriate application; posts containing the unverifiable references were deleted.

Participants were advised of the unverifiable references and related posts were deleted to maintain academic integrity and avoid any inadvertent re-use of the posts or arguments purportedly supported by them.

Results from Assignment Submissions

Examining assignment submissions compliant with the attestation requirement revealed the use of inherent AI tools in desktop software by two thirds of the participants. One student noted use of a survey tool’s AI assistant in designing an assessment tool. Other students attested to using AI tools for research, alternative explanations to required readings, draft outlines, and grammar-checking.  

With the plethora of AI tools in the market place, it is not surprising that several AI tools reported in the attestations were new to me. Participants are mostly employed professionals and their available software is often a reflection of employer-determined deployments rather than personal choice.

Overall, submissions attesting to appropriate use of AI tools were straightforward to assess against grading criteria established for the course.

Discussion

From this experience, a  policy accepting certain AI uses can enhance assignment quality. Discussion submissions reflected good levels of knowledge demonstration, application of concepts, and timeliness-- similar to past performances in this course.

Declared use of AI tools was also associated with other factors such as high access of required and optional course resources.  This may simply reflect the diligence of these particular participants rather than any impact regarding AI.

Interestingly, in more than a decade of teaching this and similar courses, this was the first time I received no requests for time extensions to discussion or assignment submission deadlines. It is not clear to me why this was the case or if permitted access to AI for uses such as research and alternative explanations played a role in this observed difference from past patterns.

The detection of unverifiable references indicates a more fundamental failure in understanding of academic integrity protocols. Ironically, referencing an AI hallucination reveals the student did not access the cited work--something that might not have been detectable if a verifiable reference had been used. This suggests a need for further training in proper use of references as well as AI literacy around issues such as hallucinations.

Final Thoughts

This instructional experience reinforces the need for increased AI literacy in PI&DM education. This means moving beyond permissive policies to intentional course design integrating AI issues into the curriculum.

The alternatives are not tenable. Banning all use of AI tools in continuing education courses is impractical. AI is intrinsically present in everyday apps and likely in participants' available software environment.  Enforcement of extensive restrictions is difficult and really detracts focus from assessment of mastery. There are “AI detectors” available to instructors, but authentic, well-written original work fed into these will often result in  scores in the “likely AI-generate” range of the scale. (And, of course, such detectors may be defeated by asking  an AI app to “humanize” work to score lower on the “likely AI-generated” scale).

In a work world where AI tools are increasingly intrinsic, AI literacy and understanding of academic integrity must be priorities. Permitting use of AI tools is merely accepting reality. That’s easy. The hard part is incorporating AI technologies into the curriculum and improving AI literacy among PI&DM professionals… including instructors.