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Showing posts tagged with: Harvard

AI’s Impact On Critical Thinking and Learning – What Studies Are Saying So Far

jerry9789
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artificial intelligence, Burning Questions

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Generative AI and Critical Thinking

On our last blog, we touched on two studies suggesting that Generative AI is making us dumber.  One of those studies, which was published in the journal Societies, aimed to look deeper into GenAI’s impact on our critical thinking by surveying and interviewing over 600 UK participants of varying age groups and academic backgrounds.  The study found “a significant negative correlation between frequent AI tool usage and critical thinking abilities, mediated by increased cognitive offloading.” 

Cognitive offloading refers to the utilization of external tools and processes to simplify tasks or optimize productivity.  Cognitive offloading has always raised concerns over the perceived decline of certain skills — in this instance, the dulling of one’s critical thinking.  In fact, the study found that cognitive offloading was worse with younger participants who demonstrated higher reliance on AI tools and less aptitude when it comes to their own critical thinking skills.  

Conversely, participants with higher educational backgrounds showed better command of their critical thinking no matter the degree of AI usage, putting more confidence in their own mental acuity than the AI-based outputs.  Aligning with our advocacy for the “appropriate use of AI,”  the study emphasizes the importance and appreciation of high-level human thinking over thoughtless and unmitigated adoption of AI technology.  

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Generative AI and Learning

In truth, a number of earlier studies have revealed that the arbitrary adoption of AI tools can be detrimental to one’s ability to learn or develop new skills.  A 2024 Wharton study on the impact of OpenAI’s GPT-4 demonstrated that unmitigated deployment of GenAI fostered overreliance on the technology as a “crutch” and led to poor performance when such tools are taken away.  The field experiment involved 1,000 high school math students who, following a math lesson, were asked to solve a practice test.  They were divided into three groups, with two of these groups having access to ChatGPT while the third had only their class notes.  One group of students with ChatGPT performed 48 percent better than those without; however, a follow-up exam without the aid of any laptop or books saw the same students scoring worse by 17 percent than their peers who had only their notes.  

What about the second group with the GenAI tutor?  They not only performed 127 percent higher than the group without ChatGPT access on the first exam, but they also scored close to the latter during the follow-up exam.   The difference?  Sometime down the line of their interactions, the first group with ChatGPT access would prompt their AI tutor to divulge the answers, resulting in an increased reliance on GenAI to provide the solutions instead of making use of their own problem-solving abilities.  On the other hand, the other group’s AI tutor version was customized to be closer to how real-world and highly effective tutors would interact with students: it would help by giving hints and providing feedback on the learner’s performance, but it would never directly give the answer.  

Similar tests with a GenAI tutor in 2023 studied the same issue of AI dependence and the value of careful deployment of AI tools.  Khanmigo, a GenAI tutor developed by Khan Academy, was voluntarily tested by Newark elementary school teachers, who belong to the largest public school system in New Jersey.  They came back with mixed results, with some complaining that the AI tutor gave away answers, even incorrect ones in some cases, while others appreciated the bot’s usefulness as a “co-teacher.” 

Other studies regarding the effectiveness of AI tutors have shown increases in learning and student engagement.  These studies have also shown that GenAI can help reduce the time it takes to get through learning materials compared to traditional methods.  One study that extolled the benefits of GenAI tutors involved Harvard undergraduates learning physics in 2024, and similar to the third group in the Wharton research, the AI was prevented from directly providing the answer to students.  It would guide the student throughout the learning process one step at a time, providing incremental updates of the student’s progress, but never outright telling them the answer.  There are merits to the idea of Generative AI as a teaching assistant, but it serves students better when it is positioned to engage one’s attention and abilities rather than induce dependence on it to generate the answers.

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Can We Use GenAI Without Making Us Dumber?

These studies shed light on how we should approach AI solutions and development, whether the end product is being deployed in learning, productivity or other relevant applications.  Beyond thoughtful planning and considerations on how AI tools would be deployed, there should be a focus on engaging the human faculties involved, with safeguards empowering man throughout the entire process instead of letting the machine take over the process wholesale.  AI technology is developing rapidly, but we can keep pace and remain reasonable as long as human engagement and empowerment is kept at the core of its utilization and adoption.  

Amid contemporary fears that anyone could be replaced anytime by AI, these studies highlight the importance of how vital and interconnected the human factor is to the effective deployment and development of AI tools.  One could be content with the constant and consistent output AI tools generate, but progress is only possible when competent human minds are involved in the process and direction.  Students can easily find answers with AI tools at their disposal, but why not advance their understanding of how solutions are formed with engaging and relatable AI-powered educational experiences?  High-level human thinking grounded by values and experience can’t be replicated by machines, and perhaps there’s no better time than now to incorporate it into the heart of the AI revolution. 

While AI development hopes that optimization and automation free the human mind to go after bigger and more creative pursuits, we here at Cascade Strategies simply hope that humanity emerges from all of these advancements more and not less than what it as we entered the AI revolution.

 

 

Additional Reading:

Why AI is no substitute for human teachers – Megan Morrone, Axios

AI Tutors Can Work—With the Right Guardrails – Daniel Leonard, Edutopia  

 

 

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4 Trends Indicate AI Is Disrupting Labor Market

jerry9789
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artificial intelligence, Burning Questions

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Is AI going to disrupt the labor market?  Researchers think it has already started to do so.  

Harvard economists David Deming and Lawrence H. Summers, along with Kennedy School predoctoral fellow Christopher Ong, have presented a new paper that looks at over 100 years of “occupational churn” for a study of technological disruption.  “Occupational churn” refers to each profession’s share in the U.S. labor market, which Deming and Summers have always been interested in gauging.  With the help of Ong, they applied the metric to 124 years of U.S. Census data, initially sharing their findings in a volume published last fall by the Aspen Economic Strategy Group.  

So are robots going to take over human jobs?  This sentiment has always been present, and for good cause, whenever breakthrough technologies such as keyboards, electricity, and computer-based manufacturing emerge.  The 1950s, ’60s, and ’70s demonstrated volatility that surprised but eventually made sense to Summers, while Deming characterized the 2000s and 2010s as having “automation anxiety.”  The study, however, revealed that the labor market enjoyed stability and low churn between 1990 and 2017, when the pace of disruption slowed.

But for 2019 onwards, it appears that there’s a major change set to happen, with four labor market trends pointing towards AI as the new breakthrough technology. 

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1. High-paying jobs are on the rise

The first trend sees job polarization being replaced by general skill upgrading.  Job polarization refers to increased employment opportunities in the high- and low-skill occupations, while middle-skill jobs go through a relative decline.  Extending across multiple decades and various economic states, this phenomenon has been influenced by technological shifts such as manufacturing automation and the widespread adoption of office software.  

However, the report noted that between 2016 and 2022 low- and middle-skill jobs have both declined, while high-skilled, high-paying jobs have slightly increased.  The report adds that data collected through 2024 show similar results, denoting the end of polarization as of 2016 and the start of a trend toward skill upgrading. 

2. Low-paying service work employment is flat or in decline

Job polarization during the 2000s was seen as a result of middle-skill production jobs being replaced by low-paid service work.  Low-skill jobs enjoyed robust growth during the 2000s but slowed in the early 2010s and was flat throughout the rest of the decade, falling rapidly in 2020 when the COVID-19 pandemic happened.  While low-paying occupations have partly recovered in 2024, most service sector employment is back to the same level it started at before its rapid growth back in the 2000s.  

The decline in low-paying service work can’t be pinned solely on the emergence of AI, however, as other factors include the aforementioned pandemic disruption, increasing wages, and a tighter labor market. 

3. STEM occupations are on the rise

After a decline in the 2000s, STEM (science, technology, engineering, and math) jobs are now enjoying rapid growth from 6.5 percent in 2010 to nearly 10 percent in 2024.  This growth also extends to business and management occupations such as science and engineering managers, management analysts, and other business operations specialists.  

Firms have also increased their investments in AI-related technologies to match the rising number of technical talents they’re hiring and developing.  Mostly driven by the need for more computing power, software and information processing investments are now above 4 percent once again — the same level they were at prior to the bursting of the dot-com bubble and the 2001 recession — while research and development spending as a share of GDP has now reached a record high of 2.9 percent. 

4. Retail sales jobs are in decline

Even before the pandemic, retail sales occupations had been declining.  Retail sales jobs dropped by 850,000 between 2013 and 2023, which translates to a decrease from a 7.5 percent share to a 5.7 percent share of employment and represents a 25 percent reduction of share in the job market in just a decade.  

This reduction is seen as one effect of e-commerce’s early adoption of predictive AI models around the mid-2010s to generate personal recommendations based on customers’ browsing and buying histories, along with predicting local product needs for stocking warehouses.  Online retail has more than doubled its share of all retail sales, increasing to 15.6 percent from 7 percent in 2015.  

Meanwhile, retail labor productivity has increased while the number of jobs declined, mimicking what had happened with manufacturing production jobs 50 years prior.  Online retail’s demand for light delivery service truck drivers for their last-mile package delivery and “stockers and order fillers” in their large warehouses resulted in employment growth in these occupations. 

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Conclusion

Is AI going to replace you at work?  Looking at these four trends, the answer is going to depend on what you do for a living.  

Summers pointed out that that “highly empowering” forms of AI might be so transformative that “certain types of activities simply won’t be done by people anymore.”  Data exists to corroborate the assertion that automation claims jobs, Deming citing early 20th-century telephone operators in a Substack post.  The study notes that sales and administrative support occupations may experience future declines in employment as AI innovates and improves on certain tasks — for example, personalized product recommendations, rapid pricing adjustments, inventory management, transcription, and automated scheduling. 

As AI is increasingly used to boost productivity, there are some tasks where it still might not be as effective as humans.  Based on this observation, some firms may conclude that, rather than replace human knowledge workers with robots, it might be wiser to simply increase their expectations from their human workforce.  This is one of the reasons the study recommends higher levels of public and private investment in STEM education, reasoning that such training and reskilling of workers will help them adapt to a world in which the new technologies are here to stay.

 

Additional Reference Article: Is AI already shaking up labor market? – The Harvard Gazette, Christy DeSmith (February 14, 2025)

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