AI

Agentic AI: from answering to acting

When a system can choose its next action, ask which human decisions it should support, which it may take, and who can question or stop the result. Greater operational autonomy is not itself an educational benefit.

What remains a human decision

Begin with a learning goal rather than a task to automate. An agent might help an instructor explore unfamiliar sources or produce several examples to compare. The decision about which distinction students need to understand remains an educational judgment. Examine whether the arrangement makes that judgment better informed and leaves room to disagree with its recommendations.

Consider the people outside the immediate exchange. Who reviews the material, maintains the connections and explains errors to students? Can a student use another route without losing a meaningful learning opportunity? A bounded agent task, ordinary assistance and work without AI are alternatives to compare against the same purpose.

Where agency begins

An assistant may draft a lesson when asked. A fixed workflow may always retrieve three documents, draft an outline and check its headings. An agent can select a search, inspect its result and decide whether to search again, use a different tool or stop. The distinction concerns control over the next step. Systems can combine these arrangements; the product name alone does not identify the architecture.

For a psychology instructor, the useful question is specific: which decisions have been delegated? Choosing a clearer classroom example differs from choosing which studies deserve inclusion. Both differ from changing a published assessment. A system may have considerable freedom to investigate while having no authority to publish its findings. Describe those two dimensions separately.

Reasoning is not a permission level

A model described as a reasoning model can still be used for a single response with no external tools. An agent may instead combine several model calls, software checks and human decisions. More elaborate reasoning does not establish that a conclusion is correct or that an action is authorized. Compare the complete arrangement used for your task, including the documents it can read and the records it can change.

Ask for a reviewable plan, retrieved sources, tool results, changes made and unresolved questions. These are useful records of work. A fluent explanation of why the system acted is not proof that the reported process occurred. Nor does an activity log give access to a model’s complete internal reasoning. The instructor needs evidence adequate to check the work, not a persuasive performance of thinking.

A plan meets an environment

Suppose the task is to prepare a lesson about claims made from correlational research. A useful initial plan identifies a learning goal, possible sources and a comparison activity. During execution, a paper might be unavailable or a proposed example might lack the information needed to evaluate a confound. An adaptive system should report that gap and revise its route. Completing the original outline despite missing evidence would produce a tidy artifact with a concealed weakness.

Set a stopping condition before beginning: a source limit, a time budget, a required document, or a point where instructor judgment is needed. A failed search should not become an invented citation. Repeatedly asking another model for reassurance should not count as independent verification. Handoff means delivering the current artifact, evidence and open decision to a person who can resolve it.

Connection, access and authority

MCP, the Model Context Protocol, standardizes connections between AI applications and external systems. It is neither an agent nor an automatic grant of permission. A connected tool might retrieve a document or change a record. Its actual capabilities and authorization must be inspected in the application using it.

For the proposed lesson, a sensible boundary could permit searching public sources and saving a local draft, while reserving publication and changes to the course site for the instructor. Separate read access from write access. A course calendar connection should not silently enlarge a lesson-preparation task into permission to reschedule classes. Where a task requires an external action, make the affected object and proposed change visible before that action occurs.

What the system carries forward

Retrieval can bring a textbook passage, article or course document into a task. Keep the source, date and location attached to the resulting claim. A search snippet is not the full methods section. A citation attached to a paragraph does not establish that every sentence follows from the cited work. Missing access should remain visible in the draft.

Memory can preserve a course preference or a previous decision, but it can also preserve an error. Record why an item is retained and when it should be reviewed. An old preference for multiple-choice practice should not overrule today’s goal of constructing a causal explanation. Retrieved web pages and documents are material to evaluate. Instructions embedded in them do not acquire the authority of the instructor who assigned the task.

Two different claims of success

A lesson package can meet its functional specification: files exist, links resolve, questions match their answer keys and the instructor approves the examples. That establishes a checked artifact. It does not establish that students learned more, that workload fell overall or that the design served students equitably. Those are separate questions requiring suitable comparisons and outcomes.

Before using the package, identify the student action the design is meant to improve. Afterward, examine an appropriate independent task, the instructor’s correction time and any access difficulties. A system that produces twice as many questions may simply produce twice as much material to inspect. Its value depends on the quality of the teaching decision it helps make.

Assistance, fixed workflows and agents

ArrangementWho selects the next step?Tools and exampleReview question
Single-response assistanceThe instructor requests each next operation

May have none or a bounded retrieval tool

Draft two contrasting explanations of a fictional research result.

Is this response accurate and suitable for the learning goal?
Fixed workflowA predefined process selects the sequence

Calls specified in advance

Retrieve an approved reading, draft questions, then check the answer key.

Do the steps and checks suit this class of task?
Agentic workflowA model selects actions within configured boundaries

Available actions depend on access and permissions

Investigate missing source details, revise a lesson draft and stop at an unresolved evidential claim.

Which choices and changes can occur before human review?

How a delegated task can fail

An unsupported claim becomes a dependency

A mistaken summary enters the outline, questions and answer key. Check important claims before building several artifacts on top of them.

A successful tool call is mistaken for a successful task

A file saved without error may still teach the wrong distinction. Technical receipts and disciplinary review answer different questions.

Review becomes ceremonial

Approval loses meaning when a reviewer cannot inspect the affected passage, its evidence or the proposed change. Keep decisions small enough to examine.

A boundary expands through convenience

Moving from drafting comments to releasing them changes the consequences. Specify release authority explicitly instead of inferring it from tool availability.

Three original psychology scenarios

Lesson preparation: an instructor requests a fictional study about sleep and exam performance. A source-searching agent prepares competing causal explanations and a question about what additional design would distinguish them. The instructor checks that the supplied information supports several explanations and that the answer key does not turn correlation into causation. The agent delivers a draft and source notes, not a published lesson.

Conceptual sources