This has become an increasingly concerning question not just in education but in business as well. A number of thought leaders have raised worries about the reliance on AI hollowing out the rising generation of managers and company leaders. Junior staff members who perform a given set of tasks don\u2019t just perform them\u2014they learn from that performance, from failure and success and collaboration. If those junior staffers are replaced by AI, or by staffers who perform those tasks using AI, will they still gain the hard-learned experience they need to succeed at higher leadership levels? <\/p>\n
\u201cWhen technology automates a task,\u201d the Wharton authors write, \u201chumans can miss out on valuable experience, inducing a tradeoff where the technology improves performance on average but introduces new failure cases due to reduced human skill.\u201d <\/p>\n
Like the risk of pilots overusing autopilot<\/p>\n
The study uses the example of airline pilots: \u201cOverreliance on autopilot led the Federal Aviation Administration to recommend that pilots minimize their use of the technology; their precautionary guidance ensures that pilots have the necessary skills to maintain safety in situations where autopilot fails to function correctly.\u201d<\/p>\n
The risk of skill loss then doubles back on itself: Generative AI continues to suffer from hallucinations, instances where its output (answers) are presented as confidently truthful but are factually incorrect. A student, staffer, or leader who has gradually de-skilled, or failed to learn due to an overreliance on AI, will not have sufficient knowledge to spot the error and correct the hallucination. <\/p>\n
What about using it in \u2018study mode\u2019? <\/p>\n
AI developers aren\u2019t unaware of these issues. About a year ago OpenAI released ChatGPT Study Mode, which the company said would “guide students towards using AI in ways that encourage true, deeper learning.”<\/p>\n
Chase DiBenedetto, an experienced tech reporter for Mashable, put Study Mode to the test shortly after its release. In her article, \u201cI tried learning from AI tutors<\/a>,\u201d she found mixed results. <\/p>\n DiBenedetto reported that Study Mode\u2019s brief interactions and minimalist user experience \u201cmake it easier to process what you are learning.\u201d The product offered helpful practice tests, quick overviews, and seemed \u201cbuilt for learners seeking clarification on rubrics and grading standards.\u201d <\/p>\n The downside: DiBenedetto found that ChatGPT Study Mode \u201cwould frequently give the answers, unprompted, and failed to let users fix mistakes before moving them on to the next step.\u201d She found the experience frustrating, in part because \u201cthe chatbot is obsessed with getting users to practice and perfect what they just \u2018learned.\u2019\u201d <\/p>\n The Mashable writer reported her experience to Hamsa Bastani, the lead author of the Wharton study. The researcher seemed unsurprised. DiBenedetto wrote: <\/p>\n \u201cAcross the board, Bastani says she has yet to come across an \u2018actually good\u2019 generative AI chatbot built for learning. Of the studies that have been done, most are negative or negligible as far as learning improvement.\u201d<\/p>\n Follow-up research: can a better tutor be designed? <\/p>\n Two years after releasing the initial Wharton study, Bastani followed up with further intriguing work<\/a>. Working with PdD candidate Angel Tsai-Hsuan Chung, Bastani explored small design changes might make AI tutoring more effective by emulating the most effective practices of human instructors.\u00a0<\/p>\n