Artificial Intelligence (AI) can now do more than draft text: It can navigate entire online courses, complete tests, and provide feedback on presentations. This affects students and workers alike because the same tools can shift from useful support to taking over a task completely. The crucial issue is therefore not simply whether you use AI, but which parts of the process you allow it to control.
Why maintaining control matters
Agentic AI refers to systems that carry out multistep tasks independently instead of answering one isolated question. According to a report on AI agents in online courses, students are connecting such tools to learning platforms including Canvas, Brightspace, and Blackboard. An instruction such as “Log in and complete the quiz” may be enough for an agent to analyze course material, answer multiple-choice questions, and, according to interviewed instructors, even chat with other students.
This moves the boundary between assistance and substitution. A chatbot might explain a confusing passage, while an agent can produce supposed evidence that you understood the material. The result may look correct on screen but reveal little about whether you could complete a similar task without the tool.
Reports about these cases do not represent all AI use, however. In a Studyflix survey of 1,012 people, seven out of ten respondents said they would never let AI write an entire exam. At the same time, 22 percent said they sometimes had AI complete full assignments, while 19 percent considered having to think less an advantage.
The two sources therefore create tension rather than a direct contradiction: Extensive automation and cheating occur, but they do not necessarily describe the majority’s behavior. The survey on AI literacy among young people also points to a teaching gap. Seventy-four percent said they had taught themselves how to use AI, while only four percent had learned the relevant skills from an instructor.
What AI can do for learning and work
Generative AI, meaning systems that produce text and other content, is most useful when it supplements an exercise rather than replacing the learning objective. One specific example comes from an eight-week entrepreneurship program at Harvard Business School. In the $699 HBS Foundry bootcamp, participants practice pitches and simulated board meetings with AI avatars of instructors, while the program also includes weekly live sessions with teachers.
This arrangement illustrates a practical division of labor. Repeatable practice becomes easier to access, while human instructors remain involved. The avatars were created by HeyGen and made more guided after trial participants asked for more direction than a basic chatbot provided. The report does not independently establish that their feedback matches human instruction in quality; one instructor whose likeness was used also described his digital copy as a little “creepy.”
At work, the most immediate benefit often comes from routine tasks. A study of actual browsing behavior across more than 200,000 US households between 2021 and 2024 associated ChatGPT use with educational platforms, specialized job sites, and government information pages. The report cites help with a tax return as an everyday example. Researchers calculated productivity gains of 76 to 176 percent for targeted AI assistance on home computers, although these US findings cannot automatically be applied to every activity or country.
Saving time also does not guarantee a better outcome. According to the analysis of real-world usage data, much of the time gained flowed into social media services such as TikTok and Instagram. AI can free up minutes, but it does not decide whether you spend them on deeper learning, additional rest, or simply more scrolling.
Pros and Cons of AI as a tool
Pros:
- Faster routine work – AI can shorten repetitive tasks and potentially leave more time for demanding parts of the job.
- More opportunities to practice – AI avatars can repeatedly support pitches or simulated meetings without tying every session to a live appointment.
- Lower barrier to getting help – Explanations and feedback are immediately available when no teacher or specialist can assist you.
- Broad support – Observed uses range from education and job searches to mandatory interactions with government services.
Cons:
- Outsourced understanding – Automating courses and exams can produce a result without building the underlying skill.
- Less thorough work – Saving time may encourage people to start more projects while spending less effort improving each one.
- Limited guidance – When learners largely teach themselves, shared rules for verification, disclosure, and permitted assistance may be missing.
- Uncertain value of saved time – Free time does not automatically go toward more valuable work and may disappear into social media instead.
How to use AI with clear boundaries
Step 1: Define the real learning or work objective
- State what you must be able to explain, decide, or perform yourself after completing the task.
- Separate practice and routine elements from the part intended to demonstrate your own ability.
- Do not let an agent operate a learning platform, exam, or full assignment when that process is itself being assessed.
Step 2: Give AI a supporting role
- Use it for an explanation, a practice question, or additional feedback on a rehearsal presentation, for example.
- Ask for specific criticism rather than a finished product that you merely submit.
- Complete at least one comparable step without AI so you can see whether you genuinely understand the material.
Step 3: Check the result and the time saved
- Compare the output with the provided learning material or the stated requirements of your task.
- Record which parts came from you and which came from the tool if your school, university, or employer requires disclosure.
- Allocate saved time deliberately to revision, further analysis, or rest instead of treating it as an automatic productivity gain.
This approach cannot prevent every incorrect or superficial output. It does keep responsibility with you and reduces the risk of mistaking a convenient result for acquired competence. The less glamorous detail is that checking still counts as work, even when AI is involved.
What this means in practice
If you are a beginner, start with a limited task whose result you can assess yourself. You might ask AI to explain a difficult section of existing course material, then summarize it in your own words without the tool. At work, you can use AI to produce an initial draft or structure, but you should verify its claims, requirements, and conclusions yourself.
As an advanced user, you can separate several roles: AI creates a draft, critiques it in a second pass, and you make the final revision decisions. For a presentation, it can provide repeated feedback on a rehearsal, as in the Harvard program. The added value comes not from automating as much as possible, but from adding useful review cycles you might otherwise have skipped.
Adaptability also remains central in professional settings. OpenAI’s chief economist leads a team of more than ten people studying AI’s effects on workers, businesses, and institutions. According to the report on the team’s changing job profile, new system capabilities continually redirect its questions; the possibility of AI systems improving themselves was not part of its work a year earlier.
A theoretical model from academic research adds another warning. Even if large language models, meaning AI systems trained on text, worked without errors and at almost no financial cost, researchers might reduce voluntary quality work. That could include extra experiments, deeper analysis, and better writing because saved time makes starting new projects more attractive. The report on this possible quality effect describes a theoretical model, not a demonstrated general decline in research quality.
The six sources provide no specific information about availability, language support, or data protection in Switzerland. The US usage data and the Studyflix survey therefore cannot be transferred directly to Swiss schools, universities, or workplaces. The underlying challenge still applies: Institutions need understandable boundaries for acceptable assistance and should not leave AI literacy entirely to self-study.
AI works best in education and employment when it handles explanation, feedback, and routine while leaving understanding and decisions with you. The reports show genuine time savings and new forms of practice, but also automated exams and a possible loss of thoroughness. The unresolved risk is whether schools and employers can establish skills and rules before convenient substitution becomes a normal way of working.
Sources
- Studierende lassen KI-Agenten komplette Onlinekurse absolvieren – so reagieren Lehrkräfte – Unknown, 2026-08-23
- “Wir lassen junge Menschen allein”: Warum Schüler sich den Umgang mit KI noch oft selbst beibringen müssen – Unknown, 2026-08-22
- Harvard’s $699 startup bootcamp offers AI avatars of its instructors – Unknown, 2026-08-22
- OpenAI-Chefökonom sucht Mitarbeiter für einen Job, der sich ständig verändert – Unknown, 2026-08-23
- ChatGPT spart Zeit – die wir dann für Social Media verschwenden – Unknown, 2026-08-23
- Warum KI-Modelle Wissenschaftler dazu bringen könnten, schlechter zu forschen – Unknown, 2026-08-23
