Does generative AI lead to decreased critical thinking?

Elise Racine & The Bigger Picture / Better Images of AI / Glitch Binary Abyss I / CC-BY 4.0

As I have noted before LinkedIn has emerged as the clearing house for exchanging research and commentary of AI in education. And in this forum, the AI skeptics seem to be winning. Of course the doubts have always been there: hallucinations, bias. lack of agency, impact on creativity and so on. There are also increasing concerns over the environmental impact of Large Language Models. But the big one is the emerging research into the effectiveness of Generative AI for learning.

This week a new study from Microsoft and Carnegie Mellon University found that increased reliance on GenAI in the workplace leads to decreased critical thinking.

The study surveyed 319 knowledge workers and found that higher trust in AI correlates with reduced critical analysis, evaluation, and reasoned judgment. This pattern is seen as particularly concerning because these essential cognitive abilities - once diminished through lack of regular use, are difficult to restore.

The report says:

Quantitatively, when considering both task- and user-specific factors, a user’s task-specific self-confidence and confidence in GenAI are predictive of whether critical thinking is enacted and the effort of doing so in GenAI-assisted tasks. Specifically, higher confidence in GenAI is associated with less critical thinking, while higher self-confidence is associated with more critical thinking. Qualitatively, GenAI shifts the nature of critical thinking toward information verification, response integration, and task stewardship. Our insights reveal new design challenges and opportunities for developing GenAI tools for knowledge work.

Generative AI is being sold in the workplace as boosting productivity (and thus profits) through speeding up work. But as AI tools become more capable and trusted, it is being suggested that humans may be unconsciously trading their deep cognitive capabilities for convenience and speed.

About the Image

Giant QR code-like patterns dominate the cityscapes, blending seamlessly with the architecture to suggest that algorithmic systems have become intrinsic to the very fabric of urban life. Towering buildings and the street are covered in these black-and-white codes, reflecting how even the most basic aspects of everyday life— where we walk, work, and live — are monitored. The stark black-and-white aesthetic not only underscores the binary nature of these systems but also hints at what may and may not be encoded and, therefore, lost—such as the nuanced “color” and complexity of our world. Ultimately, the piece invites viewers to consider the pervasive nature of AI-powered surveillance systems, how such technologies have come to define public spaces, and whether there is room for the “human” element. Adobe FireFly was used in the production of this image, using consented original material as input for elements of the images. Elise draws on a wide range of her own artwork from the past 20 years as references for style and composition and uses Firefly to experiment with intensity, colour/tone, lighting, camera angle, effects, and layering.

Does generative AI lead to decreased critical thinking?

Elise Racine & The Bigger Picture / Better Images of AI / Glitch Binary Abyss I / CC-BY 4.0

As I have noted before LinkedIn has emerged as the clearing house for exchanging research and commentary of AI in education. And in this forum, the AI skeptics seem to be winning. Of course the doubts have always been there: hallucinations, bias. lack of agency, impact on creativity and so on. There are also increasing concerns over the environmental impact of Large Language Models. But the big one is the emerging research into the effectiveness of Generative AI for learning.

This week a new study from Microsoft and Carnegie Mellon University found that increased reliance on GenAI in the workplace leads to decreased critical thinking.

The study surveyed 319 knowledge workers and found that higher trust in AI correlates with reduced critical analysis, evaluation, and reasoned judgment. This pattern is seen as particularly concerning because these essential cognitive abilities - once diminished through lack of regular use, are difficult to restore.

The report says:

Quantitatively, when considering both task- and user-specific factors, a user’s task-specific self-confidence and confidence in GenAI are predictive of whether critical thinking is enacted and the effort of doing so in GenAI-assisted tasks. Specifically, higher confidence in GenAI is associated with less critical thinking, while higher self-confidence is associated with more critical thinking. Qualitatively, GenAI shifts the nature of critical thinking toward information verification, response integration, and task stewardship. Our insights reveal new design challenges and opportunities for developing GenAI tools for knowledge work.

Generative AI is being sold in the workplace as boosting productivity (and thus profits) through speeding up work. But as AI tools become more capable and trusted, it is being suggested that humans may be unconsciously trading their deep cognitive capabilities for convenience and speed.

About the Image

Giant QR code-like patterns dominate the cityscapes, blending seamlessly with the architecture to suggest that algorithmic systems have become intrinsic to the very fabric of urban life. Towering buildings and the street are covered in these black-and-white codes, reflecting how even the most basic aspects of everyday life— where we walk, work, and live — are monitored. The stark black-and-white aesthetic not only underscores the binary nature of these systems but also hints at what may and may not be encoded and, therefore, lost—such as the nuanced “color” and complexity of our world. Ultimately, the piece invites viewers to consider the pervasive nature of AI-powered surveillance systems, how such technologies have come to define public spaces, and whether there is room for the “human” element. Adobe FireFly was used in the production of this image, using consented original material as input for elements of the images. Elise draws on a wide range of her own artwork from the past 20 years as references for style and composition and uses Firefly to experiment with intensity, colour/tone, lighting, camera angle, effects, and layering.

What data is there to support the use of AI in VET and Adult Education?

Photo by Dylan Gillis on Unsplash

Of course researchers and practitioners in vocational education and training (VET) and in Adult Education are long used to their secondary status compared to School and Higher Education. And although Learning Analytics has been around for quite some years now, there has been little consideration of its use in VET and in workplace learning.

Is it is good to report that the German Federal Ministry of Education and Research together with the German Federal Institute for Vocational Education and Training have funded a study on artificial intelligence offers benefits for implementing personalised and adaptive learning environments (PALE; Schumacher, 2018). PALE are digital learning systems that continuously analyse and leverage education-related data to adapt the learning environment to individual needs and constantly changing requirements

As the authors of the study, Four Perspectives on Personalised and Adaptive Learning Environments for Workplace Learning, Yvonne M. Hemmler and Dirk Ifenthaler, say"

A major challenge in designing trusted PALE for workplace learning remains the identification of reliable indicators. Indicators are variables (e.g., interests, demographics, location) that reveal useful information about learning behavior and that are processed by specific algorithms to personalize and adapt the learning environment. Reliable indicators are crucial for PALE as accurate and comprehensive information about learners and their contexts is needed to design effective interventions to support learning processes and outcomes.

The research identified three profiles as being central to the collection of data for developing and implementing personalised and adaptive learning environments. These profiles were examined against different perspectives: Pedagogical perspective, ethical perspective , data analysis perspectives and Information perspective.

The results are cautious. “Despite rich datasets and advanced analytics methodologies, not all approaches utilising artificial intelligence in education seem to be effective for workplace learning.” They conclude that so far "no wide-scale organisational implementation of artificial intelligence for workplace learning exists and no empirical evidence is available for supporting the assumption that PALE improve the performance of involved stakeholders and organisations."

However, they suggest “Interdisciplinary perspectives on adoption models as well as on pol icy recommendations may help to move the pioneering efforts on artificial intelligence for workplace learning forward”

References

Schumacher, C. (2018). Supporting informal workplace learning through analytics. In D. Ifenthaler
(Ed.), Digital workplace learning: Bridging formal and informal learning with digital tech-
nologies (pp. 43–61). Springer. https://doi.org/10.1007/978- 3- 319- 46215- 8