Generative AI is a model and technology that generates content based on user intent, and can be viewed as a tool for content creation. It can generate new text, images, music, and even videos based on user perspectives. Nowadays, a plethora of creative intelligent tools have emerged, and their widespread application has given rise to new lifestyles and work practices, with the impact on social transformation gradually becoming evident. In the field of education, the integration of generative AI not only enriches teaching content and methods but also innovates educational teaching models, bringing revolutionary changes to education. Against the backdrop of the country’s vigorous promotion of digital transformation, understanding, recognizing, and learning about generative AI has become a core aspect for teachers to enhance their digital literacy and implement core competency reforms. However, what about the safety of generative AI? How can generative AI be used effectively? The author will explain from four aspects: recognizing risks, understanding scenarios, acquiring skills, and innovating applications, to help teachers better meet new challenges and requirements, becoming smart teachers in the AI era.
Recognizing Risks:
Understanding the Working Principles of Generative AI
and Its Potential Multiple Risks
Generative AI learns and understands through large-scale datasets and deep analysis, mimicking human language and behavioral patterns to generate content that meets user needs.
Its strength lies in the diversity and creativity of the content it generates; however, it also harbors numerous risks and challenges.
Firstly, there is the risk of value orientation.
The generative AI system may absorb and amplify biases present in its training data during the training process, leading to content that carries erroneous guidance.
Algorithmic bias and the information cocoon effect are two relevant and serious issues; if the training data contains erroneous ideologies, the content generated by AI may negatively impact users’ values and judgment.
Secondly, there is the risk of data security.
Generative AI requires a vast amount of data to learn and train, which may contain privacy and sensitive information, inevitably involving data security issues. If the input student information is not properly protected, it may lead to student privacy breaches. Additionally, a lack of clear data management regulations may result in improper information management and usage, leading to data misuse or unauthorized use.
Finally, there is the risk of content quality.
The essence of content generated by generative AI is pattern prediction, relying on a massive amount of high-quality data for learning and content generation. In the current deep implementation of curriculum reform, if the data used for AI training (including policies, theories, and case studies) is not comprehensive or of poor quality, the generated content may exhibit significant deviations or uncertainties.
For teachers, if they do not possess sufficient professional literacy and experience to reasonably evaluate and judge AI-generated content, there could be serious misleading risks, negatively impacting students.
To address the aforementioned risks and challenges, some higher education institutions and international organizations have begun to formulate guidelines for the use of generative artificial intelligence, such as UNESCO’s 2021 publication of the