Research Spotlight: How AI Literacy Shapes Students' Confidence About the Future of Work

The article "Artificial Intelligence Literacy and Perception of Employment Prospects among Final-year Undergraduates" by Munn Hong Lam, Mei Peng Low, and Keen Yew Chan explores this question among final year undergraduates in Malaysia. We spoke with one of the authors, Munn Hong Lam of Universiti Tunku Abdul Rahman, to learn more about how AI literacy shapes students' perceptions of employment prospects.

For universities, the paper highlights a practical curriculum challenge. Students may recognize AI tools and even use them often, yet still feel unsure about how to judge AI output, apply AI to real problems, or translate AI familiarity into workplace confidence.

This matters for employers and policymakers because AI readiness is not only a technical issue. It involves confidence, attitudes, discipline specific exposure, and anxiety about whether entry into the labor market will reward adaptability or leave some students behind.

Munn Hong Lam reflected on the broader significance of the study: "The biggest implication is that AI literacy should go beyond mere awareness. Many students may already be aware of what AI is and may even use AI tools, but they still need guidance to understand how to evaluate the outputs produced by AI, how to apply them in their particular fields, and how to feel confident in applying AI in the workplace. Therefore, universities and employers need to focus on developing the students’ ability to make sound judgments in practice rather than just having a basic understanding of how to use AI."

The article's main contribution is its careful attention to the tension between awareness and confidence. Three findings stand out.

Awareness was high, but applied skill was less secure. In the survey, students reported strong awareness of AI tools and concepts, with the highest mean score for awareness of AI tools. Confidence in evaluating AI generated output was lower, which points to a gap between knowing about AI and using it critically.

Students were generally positive about AI's value for employability, but job security remained uncertain. Respondents strongly agreed that employers will value graduates who are literate in AI and that AI improves productivity, while responses on whether future jobs will remain secure were more cautious.

Program of study appeared to shape how students interpreted AI's role. The engineering student in the qualitative phase described AI as a tool that could support adaptation, while the business student expressed concern that AI could replace routine and creative tasks. Because this was a pilot study, the pattern is suggestive rather than conclusive.

Interestingly, Munn Hong Lam reflected on the finding that most deserves wider attention: "The main thing that I found was the difference between awareness and confidence. Most students were at least familiar with AI tools and could see the value of AI, but their confidence in applying AI critically and responsibly was still uneven. This difference illustrates that having the necessary skills to use AI is only part of what an individual needs in order to be ready to use AI; they also need to have confidence in using AI."

The authors used a sequential mixed methods design that began with semi structured interviews and then moved into a questionnaire survey. The qualitative phase included two final year undergraduates from different disciplines, one business student and one engineering student, with English language interviews lasting about 15 minutes each.

The interview recordings were transcribed verbatim and analyzed thematically, then used to add interpretive depth to the quantitative results. The survey drew responses from 13 final year bachelor students, used six point Likert items on AI literacy, attitude toward AI, and perceived employment prospects, and analyzed the measurement model in SmartPLS 4 because the pilot sample was too small for full structural testing.

The article explicitly mentions Evidano in the analysis stage. The authors write that "the qualitative interview data were entered into Evidano and were interpreted thematically," using that qualitative strand to add richness to the quantitative survey results.

That wording is important because it positions Evidano as part of a broader mixed methods workflow rather than as a substitute for research judgment. The authors selected the research question, designed the interviews and survey, interpreted the themes, and connected those themes to the conceptual framework on AI literacy, self efficacy, attitudes, and employment prospects.

Munn Hong Lam also reflected on how AI tools can support careful research workflows when used responsibly: "The qualitative element of our research was supported by Evidano through assistance with thematic organisation and interpretation of interview data. This was helpful in our research workflow, particularly in identifying patterns within the responses received during the interviews, which then supported the broader mixed-methods design of the study. I think researchers should generally consider AI-assisted analysis to be a useful tool rather than a substitute. It is still important to rely on their experience, intuition, knowledge, and understanding of the context and purpose in developing the final analysis."

For educators, the study suggests that AI education should move beyond basic awareness. Students need structured practice in evaluating outputs, applying AI to domain specific problems, and understanding the ethical and practical limits of automation.

For researchers, the article also shows the value of combining qualitative and quantitative evidence. Interviews capture the language of fear, confidence, and adaptation, while survey items show where awareness, attitude, and perceived job security may diverge across students.

Looking ahead, Munn Hong Lam summarized the takeaway for universities and students: "One of the most important insights is that it will help students to develop the necessary knowledge and experience they need to contribute meaningfully in a workplace that has been influenced by AI, only when AI literacy is connected to confidence, practical skill sets, and discipline-specific learning. Students do not just need to know about AI; they need to understand how to work with it, question it, and use it in ways that strengthen their future employability." The deeper message is that AI readiness is not only about learning tools. It is about helping students build the confidence, judgment, and resilience they need to enter a changing workplace.