Editor’s Note
As the saying goes, “Half the knowledge is dangerous.” A teacher’s life is about learning. In recent years, many experts have conducted in-depth research on the professional development of teachers. These multi-faceted reflections are of great significance in promoting lifelong learning and professional development for teachers, enhancing their ability to cultivate virtue and nurture talent for the country. Therefore, the “Micro Words National Training” has launched the “Expert Voices” column, publishing articles related to education and teaching, helpful for teaching research training, and beneficial for teacher development, hoping to assist teachers in refining their knowledge and promoting teaching through learning. Today, we publish an article by Associate Researcher Wei Fei titled “How to Use Generative AI to Become a Smart Teacher,” hoping to inspire a wide readership.
How to Use Generative AI to Become a Smart Teacher
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 produce new texts, images, music, and even videos based on user perspectives. Nowadays, a plethora of creative intelligent tools have emerged, and their widespread application has led to new lifestyles and work methods, with gradual impacts on social transformation. In the field of education, the integration of generative AI can enrich teaching content and methods, innovate educational models, and bring 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 component for teachers to enhance their digital literacy and implement core competency reforms. However, what about the safety of generative AI? How can it be used effectively? The author will elaborate on 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.
01
Recognizing Risks:
Understanding the working principles of generative AI and its potential multiple risks
Generative AI learns and deeply analyzes large datasets, mimicking human language and behavior patterns to generate content that meets user needs. Its power lies in the diversity and creativity of the content it generates; however, it also harbors many risks and challenges.
First is the risk of value orientation. Generative AI systems may absorb and amplify biases present in their training data during the training process, leading to generated content with incorrect orientations. Algorithmic bias and the information cocoon effect are two related issues that cannot be ignored. If the training data contains erroneous ideologies, the content generated by AI may negatively impact users’ values and judgment.
Second is the data security risk. Generative AI requires a large amount of data for learning and training, which may include private and sensitive information, inevitably raising 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 norms can lead to improper information management and usage, resulting in data misuse or unauthorized use.
Lastly, there is the risk of content quality. The essence of content generated by generative AI is pattern prediction, relying on a vast 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 cases) is not comprehensive or of poor quality, the generated content may exhibit significant biases or uncertainties. For teachers, without sufficient professional literacy and experience to reasonably evaluate and judge AI-generated content, there may be serious risks of misleading students, leading to negative impacts.
To address these risks and challenges, some higher education institutions and international organizations have begun to develop guidelines for the use of generative artificial intelligence. For example, UNESCO released the “Ethical Issues in Artificial Intelligence: Recommendations” in 2021 and the “Guidelines for Generative Artificial Intelligence in Education and Research” in 2023, providing important learning resources for teachers. Teachers should strengthen their awareness of data ethics and security, actively engage in professional learning, enhance their professional literacy, and continuously improve their sensitivity and judgment regarding the ethical and professional use of generative AI, ensuring compliance with ethical safety and professional standards, which is a fundamental prerequisite for educational applications.
02
Understanding Scenarios:
Familiarizing oneself with typical educational application scenarios of generative AI
Generative AI trains on large datasets, including educational theories and practical cases, teaching methods and strategies, online literature resources, etc., to generate creative suggestions based on educational concepts and best practices, effectively assisting teachers in their routine practice. Typical application scenarios of generative AI include: optimizing teaching design, assisting resource development, constructing inquiry environments, analyzing student data, enhancing management efficiency, and empowering research topics.
In teaching design, based on the analysis of teaching objectives and target audience, teachers can use generative AI to assist in designing key elements such as unit big concepts, higher-order questions, real-life contexts, learning scaffolds, and learning activities, to implement core competency cultivation and achieve high-quality education; in resource development, AI can support the design and creation of presentations, images, and videos, enriching resource forms and improving resource development efficiency; in constructing inquiry environments, AI can create highly interactive environments based on virtual reality, the metaverse, and other technologies, enhancing students’ problem analysis, reflective evaluation, and critical thinking abilities; in student data analysis, AI can clean evaluation data, calculate key indicators, analyze data based on learning analysis models, and generate visual charts, allowing more focus on data understanding and student analysis; in daily management, generative AI can help teachers record and analyze classroom data, assist in writing notifications, reports, and other home-school communication emails, reducing transactional work input and enhancing management efficiency; in research topics, generative AI can assist in designing and refining research questions, hypotheses, data collection tools, and research outlines, standardizing report formats and enriching research perspectives.
03
Acquiring Skills:
Mastering key skills for operating and using generative AI
First, teachers need to master prompt engineering, i.e., writing good prompts. Generative AI needs to clearly understand human-specific needs to provide appropriate feedback; in other words, high-quality input questions can yield high-quality responses. Generative AI prompts accurately express human roles, work instructions, contexts, relevant data, and output requirements (including evaluation criteria for expected results). Work instructions are specific tasks that generative AI is expected to perform, such as designing teaching activities based on the 5E teaching model; context refers to the background information for the task, such as informing about the implementation target, duration, environmental information, etc., which also includes theoretical models, evaluation criteria, reference examples, or learning materials to guide generative AI in better responding to task requirements.
Second, teachers need to possess process thinking, articulating tasks in the language understood by large models. If the task instructions are overly complex, the questions unclear, or the requirements vague, generative AI is unlikely to produce satisfactory results. When asking questions, there should be a conscious effort to break down tasks and processes, dividing complex tasks into several specific smaller tasks. For instance, regarding the question “How to improve classroom teaching efficiency,” one can break it down from angles such as classroom management, teaching methods, teaching content, and student assessment by asking sub-questions like “How to effectively manage student discipline in the classroom?” “What teaching methods can enhance student engagement and interactivity?” “How to design learning content based on students’ levels and interests?” “What methods can accurately assess learning outcomes?”
Additionally, and most crucially, teachers must evaluate and judge the generated content. By continuously enhancing their professional literacy and judgment, they can analyze and rationally assess the accuracy of the content generated by generative AI, the implicit values, and the potential impacts on teaching and students. When faced with generative AI’s potential “hallucinations” and “serious nonsense,” teachers should possess a keen critical awareness, utilizing professional knowledge, practical experience, and literature to comprehensively review the output results of generative AI. Through a “human-in-the-loop” collaborative mechanism, they should fully participate in the answer generation process, promptly identifying and correcting errors made by generative AI and actively constructing an understanding of the issues at hand.
04
Innovative Applications:
Exploring innovative applications that deeply integrate generative AI with education and teaching
Teachers should innovate assignment and task designs to increase cognitive challenge levels. As “digital natives,” students naturally apply AI to assist their learning. The low threshold and boundary-less generative functions may encourage students’ plagiarism and dependency behaviors, which are detrimental to cultivating problem-solving abilities and critical thinking. To avoid learning inertia or the phenomenon of “having results without growth,” teachers must innovate task designs to ensure students have sufficient opportunities for cognitive training and growth space. For example, using real-life scenarios and open-ended problem-solving tasks to increase cognitive challenge levels, guiding students to conduct process evaluations and reflections to develop self-awareness and monitoring abilities.
Teachers should actively research and accumulate technical resources of generative AI to build autonomous and inquiry-based learning spaces for students. For instance, creating virtual laboratories allows students to conduct scientific experiments and inquiry activities in a safe and controllable environment; simulating various real-life scenarios helps students learn in specific contexts, enhancing the practicality and interactivity of learning; building data analysis environments enables students to explore complex relationships between data, cultivating data analysis abilities and critical thinking.
Teachers should strengthen interdisciplinary awareness in the application of generative AI, enhancing students’ comprehensive qualities. Digital technologies, especially generative AI, inherently possess the characteristic of multi-disciplinary integration, becoming an important tool for conducting interdisciplinary activities. Based on generative AI’s scenario creation, inquiry support, and personalized guidance, teachers can design and implement interdisciplinary projects. For instance, using generative AI to create historical scenarios allows students to engage in immersive inquiry-based learning; in art courses, generative AI can produce creative works, stimulating students’ creativity. Through these interdisciplinary projects, students can better understand the internal connections of disciplinary knowledge and develop innovative capabilities and critical thinking.
In conclusion, when facing generative AI, teachers need to maintain an open and cautious attitude, fully utilizing its advantages while being vigilant about its potential risks. Only by adhering to the application principle of prioritizing students’ physical and mental health and comprehensive development, and balancing technological application with ethical safety, can we ensure that it truly empowers education.
This article is the research result of the 2023 National Social Science Fund’s general project in education titled “Research on Scenario Construction and Application for Teacher Digital Competency Development” [Project Approval No.: BCA230283]
Author: Wei Fei, Associate Researcher, Deputy Dean of the Teacher Development College, East China Normal University
Source: China Education News, September 9, 2024, Page 9
