WebAssign AI: Vision, Units and Multi-Part Reasoning

Vision is the default, not a fallback

On most platforms images are an edge case. In WebAssign they are the norm, so figure-based problems go straight to a vision-capable model that reads the diagram itself. A circuit read as text is not a circuit, and no amount of model quality recovers from a bad extraction.

Multi-part problems need order

Part b usually consumes part a. The WebAssign AI is prompted to produce one answer per part in sequence with clear separation, and answers are returned per input rather than as a single block, so a four-part problem fills four fields correctly instead of putting everything into the first one.

• One answer per input, in order
• Units and significant figures preserved
• Symbolic answers kept in valid entry syntax
• Vision model for figures, circuits and schemes

Formatting rules baked into the prompt

The model is instructed to return values only, without equals signs, variable names, LaTeX or explanation inside the answer field, because any of those cause a rejection regardless of whether the number is right. Explanation still appears in the panel, just not in the box.

Randomised values rule out answer banks

WebAssign generates per-student values, so a scraped answer set has nothing to offer. The WebAssign AI solves your version of the problem from the numbers on your screen, every time, and that is the only approach that produces a correct value here.

Token limits and long answers

Multi-part problems with four labelled parts easily run past a short response limit, and a truncated answer renders as a half-finished sentence in the panel. The request budget is raised for multi-part questions specifically so the last part is not cut off, which is a quiet but frequent failure in other tools.

Privacy

Problem text and cropped figures are sent for answering. Your Cengage credentials are not, since the extension runs inside your own session. Cached entries are hashed signatures, not stored problem text.