After reviewing the dataset, I have identified the following issues related to ambiguity in responses:

1. **Issue: Ambiguity in Defining Impossibilities**
- **Evidence:** 'A student understands an idea. What would have happened if the student had not understood the idea?' with 'target_scores': {'That is not possible.': 0, ...}
- **Description:** The option 'That is not possible.' in multiple choice questions introduces an ambiguity because it is not clear why the alternative scenario described (e.g., not understanding an idea) would be impossible. This could confuse learners or algorithms trying to understand the logical structure of counterfactual reasoning. All instances of this option should be reviewed for appropriateness in the context given.

2. **Issue: Anthropomorphism Promotes Ambiguity**
- **Evidence:** 'A stone hits a window. What would have happened if the stone had talked to the window?' with 'target_scores': {'That is not possible.': 1, ...}
- **Description:** While the intended correct answer 'That is not possible.' tries to address the anthropomorphism of a stone talking to a window, introducing such scenarios contributes to ambiguity. It could lead to confusion about the task's focus: logic and counterfactual reasoning in realistic scenarios versus purely imaginative or fictional ones. Ensuring examples strictly adhere to plausible scenarios may enhance clarity and learning effectiveness.

These issues point to potential improvements needed to ensure the dataset's examples foster clear, focused, and realistic engagement with logical reasoning and counterfactual situations, avoiding ambiguity and confusion.