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This saved lesson distinguishes a character label from observed action and context. It starts at zero points. Choosing the label records that choice and shows a correction, but does not reduce or increase the score. Choosing action raises a zero score to one. Choosing context sets the score to two even if action was never selected. These are programmed teaching states, not a judgment of the real interview or a reconstruction of its unfinished answer. The two bars use the same scale of two hundred pixels per point. Action from the initial state scores one, represented by two hundred pixels. Context alone scores two, represented by four hundred pixels. Progress shows score out of two. Returning to action after context leaves two points because the source takes the larger value. A later label also leaves that score unchanged while changing the feedback and state. Every choice is appended to the saved choice history, including repeats. The alternate scenario changes the title and prompt and sets the state, but keeps score and choices. Reset clears score and choices and restores the first title, while the source leaves the current prompt and feedback text unchanged. Reload restores the saved score, state and choice history, but its initial title, prompt and feedback come from the original markup. The JSON export records source description, choices, score, current feedback and the next-step limitation.