# ChatGPT for Education and Research: Opportunities, Threats, and Strategies
## 6 Threats and Strategies
**Ethical implications and potential biases:** The ethical implications and biases of using ChatGPT in education and research should be carefully considered. Typically, LLMs rely heavily on training data, and when the data contain biases or anomalies, it could lead to unfair results. For example, if the training data are biased toward certain people or cultures, the model may produce unfair or discriminatory output. Therefore, it is imperative to ensure that the training data are diverse and well-balanced. ChatGPT and other AI language models can be used to generate fake news, hate speech, and other harmful content. This can lead to social unrest, reputation damage, and even physical harm. Furthermore, the internal mechanisms and processes are not sufficiently open and transparent to users about how they work. It is also important to ensure that the decision-making processes of these models are transparent to users. Because ChatGPT generates responses without human intervention, it can be difficult to hold anyone accountable for the responses generated. This may make it difficult to address any ethical concerns or biases. ChatGPT and other generative models involve the collection and processing of personal data, which raises concerns about privacy and data security. Appropriate measures should be taken to protect unauthorized access to individual data. **Critical thinking and problem-solving skill:** ChatGPT can generate nearly accurate answers to technical questions from a wide range of topics and correct or partially correct programming code based on problem descriptions, algorithm and problem names, etc. Simply acquiring answers and code from ChatGPT can be a barrier to improving learners' critical thinking and problem-solving skills. To the best of our knowledge, there are no such tools that can recognize code generated by AI models, and thus, the solution codes generated by AI models can be used for academic coding exams and competitions. This poses a challenge to educators on how to deal with this new situation. However, there are some strategies for determining whether responses and programming codes are generated by ChatGPT. _Look for telltale signs_: ChatGPT responses typically have certain characteristics, such as a lack of personalization or a rather generic tone. Furthermore, the programming code typically contains programming syntax and formatting. _Check for coherence_: ChatGPT responses may not have a consistent or logical flow, especially when it is generating answers to complex questions. If the answers appear disjointed or nonsensical, this may indicate that they were generated by ChatGPT or another AI model. _Compare responses_: We can compare responses generated by ChatGPT with responses generated by other language models or humans. If the answer is identical to answers generated by ChatGPT, it may be a sign that the answer was not generated by a human. _Use of plagiarism-detection tools_: we may also use plagiarism-detection tools to determine whether an answer contains programming code copied from somewhere else. This can help detect cases of fraud. In addition, if there is possible cheating with programming codes, we can ask follow-up questions to determine the depth of the learner's understanding in answering the question.