Beyond AI Knowledge: Assessing Real-World Skills

Author: Denis Avetisyan

New research highlights the need for practical evaluations to determine true AI literacy in the modern workforce.

The correlation between performance on AI User training and metrics from both the AI-LIT-M and COMP-MCQ instruments suggests these assessments aren’t merely evaluations, but active components shaping the evolving capabilities of the system itself.

A task-oriented assessment approach, aligned with real-world occupations, proves more effective in measuring AI competency than traditional knowledge-based testing.

Existing assessments of AI literacy often prioritize technical foundations over practical application, creating a disconnect between demonstrated knowledge and workplace competence. This limitation motivates the research presented in ‘AI Literacy Assessment Revisited: A Task-Oriented Approach Aligned with Real-world Occupations’, which investigates alternative evaluation methods grounded in real-world tasks. Findings from a US Navy robotics training program reveal that scenario-based assessments, simulating job-relevant AI usage, more effectively predict applied AI literacy than traditional knowledge-based tests. Could a shift towards competency-focused evaluations better prepare a non-technical workforce for the rapidly evolving landscape of AI-integrated roles?

The Inevitable Literacy Gap

Recent advances in Generative AI demand a workforce capable of critical assessment and effective implementation. The rapid pace of innovation necessitates a shift beyond basic tool usage toward nuanced understanding of AI capabilities and limitations. Traditional education often fails to provide the competencies needed for responsible AI integration, creating a critical skills gap extending beyond technical expertise to include data literacy, algorithmic bias awareness, and ethical considerations. Without focused training, individuals risk over-reliance on AI outputs, uncritical acceptance of algorithmic recommendations, and inability to mitigate potential biases—building castles on shifting sands.

Analysis reveals that neither the AI-LIT-H nor AI-LIT-MH metrics demonstrated an increase following AI User training.

The absence of focused training leaves professionals susceptible to the unintended consequences of unchecked systems.

Measuring the Unmeasurable

Effective AI Literacy Assessment is crucial for gauging preparedness and identifying training needs. Current methods relying on self-reported skill levels are unreliable. A robust, validated assessment tool is needed to objectively measure an individual’s ability to interact with and understand artificial intelligence systems. A multi-faceted approach was employed, building on existing methods like the Hornberger AI-LIT-H and refining them with the Modified Hornberger AI-LIT-MH. This assessment utilizes Scenario-Based Assessment, leveraging realistic tasks to evaluate practical AI skills and knowledge, mirroring common workplace applications.

Initial results showed that while both AI-LIT-H and AI-LIT-MH correlated with AI User quiz scores (p=0.003 and p=0.004 respectively), they did not demonstrate significant improvement after AI training (p=0.165 and p=0.28 respectively). This suggests the assessments can identify baseline knowledge but may not fully capture the impact of targeted training—further refinement is necessary to accurately measure skill development.

Beyond Knowledge Recall

The assessment incorporated competitive tasks – COMP-MCQ, COMP-DATASET, COMP-HH, and COMP-OPEN – designed to evaluate performance within realistic AI-driven scenarios. These competitions moved beyond simple knowledge recall, aiming to measure practical application of AI literacy skills. COMP-DATASET specifically focused on Data Literacy, recognizing it as foundational for effective AI model training and interpretation. The COMP-MCQ questions were guided by Bloom’s Taxonomy, structuring questions to assess a tiered range of cognitive skills relevant to AI problem-solving.

Performance on the scenario-based assessment (COMP-MCQ) demonstrated significant correlations with success across the other competitive tasks—exceeding those observed with the AI-LIT-H and AI-LIT-MH assessments. Practical application and contextual understanding are strong predictors of overall AI literacy. The competitions yielded valuable data regarding participant strengths and weaknesses.

Training for Eventual Obsolescence

The results of the AI Literacy Assessment directly inform targeted AI User Training programs designed to address identified skill gaps. These programs move beyond theoretical knowledge, focusing on practical application within specific professional contexts. Assessment data pinpoint areas where users struggle, allowing for curriculum refinement and personalized learning pathways. This training is particularly relevant for Robotics Warfare Specialists, who require a deep understanding of AI for effective operation and maintenance—understanding how AI systems function, interpreting outputs, diagnosing failures, and ensuring responsible implementation in complex operational environments.

By focusing on practical application and critical thinking, these programs aim to empower professionals to leverage AI responsibly and effectively. Continuous assessment and training cycles will be vital for maintaining a workforce capable of adapting to the rapidly evolving landscape of AI. Long stability in skillsets is, after all, the most reliable predictor of eventual obsolescence.

The pursuit of measurable AI literacy, as detailed within this study, echoes a perennial human tendency: the desire to quantify the unquantifiable. Assessments, even those grounded in practical scenarios, offer but a snapshot of potential competence, a compromise frozen in time. Robert Tarjan observed, “Algorithms must be seen as a last resort.” This sentiment applies equally to assessment methodologies. The true measure of an AI user isn’t their score on a test, but their capacity to adapt within a complex, evolving system—a capability no static evaluation can fully predict. The focus on task-oriented evaluations, while a step forward, still attempts to impose order on a fundamentally unpredictable landscape. It’s a necessary illusion, perhaps, but an illusion nonetheless.

What’s Next?

The pursuit of ‘AI literacy’ feels less like building a skill, and more like charting a coastline constantly reshaped by the tide. This work rightly questions whether current assessments measure anything beyond the memory of concepts, a fragile foundation in a field defined by relentless change. Scenario-based evaluation, tethered to concrete tasks, offers a marginally more durable signal, but it, too, is a temporary reprieve. Every task mastered today becomes a historical artifact tomorrow, a solved problem obscuring the next, unforeseen challenge.

The true limitation isn’t the assessment itself, but the illusion of complete competence. One does not ‘know’ AI; one learns to navigate its unpredictable currents. Future research should focus less on identifying what an ‘AI literate’ worker is, and more on measuring their capacity for adaptive learning – their ability to quickly unlearn assumptions and rebuild mental models. This isn’t about certifying skills; it’s about quantifying resilience.

Ultimately, any attempt to define and measure ‘AI literacy’ is a prophecy of obsolescence. The system will always outgrow the map. The field should accept this inherent instability and shift its focus from finding the literate worker to cultivating the learning organism. Order is just a temporary cache between failures, and the most valuable competency may simply be the grace to rebuild after each inevitable collapse.

Original article: https://arxiv.org/pdf/2511.05475.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/

The Inevitable Literacy Gap

Measuring the Unmeasurable

Beyond Knowledge Recall

Training for Eventual Obsolescence

What’s Next?

See also: