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When designing the final activity of this course I asked an AI tool to draft a rubric for a formative oral check in my laboratory.

What came back rewarded students who "communicate confidently and fluently" and who "show enthusiasm for the task". It reads like a normal rubric, which is exactly the problem. In my group it would have quietly favoured the students who are comfortable speaking and penalised the ones who reason well and speak hesitantly, including three adults who returned to study after years working and several students who think in Valencian or Spanish while reading documentation in English. The model does not know my class; it reproduces the average of everything written about rubrics, and that average rewards performance over reasoning.

That is where I would locate the bias discussion. We tend to look for it in what students hand in, and it also arrives through the instruments we use to judge them, especially now that those instruments are drafted in seconds.

On adapting assessment, my position is that the answer is not detection. In vocational education the useful move is to assess in the places a model cannot occupy: while the student holds the equipment, on their own sample, with a result that does not match the label. If a task can be completed by a machine that has never entered my laboratory, the task was probably measuring the wrong thing before AI existed.

Formative assessment helps in a second way too. If the only good result is the expected one, students will hand in whatever looks correct, and now they have a very efficient way of producing it. So in my classes an honest "I do not know, but I would check this" counts as a strong answer, and a wrong result with a good explanation scores as well as a clean one.

That single rule does more against copy and paste than any detector.

And a rule of my own: I tell students which parts of my materials were drafted with AI and what I changed. I ask them to declare their use, so the obligation cannot run in one direction only.

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Adapting Assessment Practices for AI-Generated Work and Algorithmic Bias

The rise of AI tools, such as ChatGPT and other text generators, has posed significant challenges for traditional assessment methods. As students increasingly rely on AI-generated content for essays, projects, and assignments, educators need to rethink how they design assessments to ensure both integrity and effectiveness. Here are some strategies to adapt assessment practices in light of this shift:

1. Incorporating Authentic Assessments

One way to adapt is by designing authentic assessments that require students to demonstrate their knowledge and skills in real-world contexts. Instead of relying solely on traditional essays or tests, educators can ask students to apply concepts in project-based learning, case studies, and problem-solving activities. These types of assessments are harder to outsource to AI tools because they require critical thinking, creativity, and personal input that goes beyond mere content generation​(

CIRCLS

).

2. Utilizing Formative Assessment Strategies

Formative assessment strategies, such as learning journals, classroom discussions, and ongoing reflections, can help address the challenge of AI-generated work. These methods involve continuous monitoring of student learning, allowing teachers to track the development of ideas and detect sudden shifts in writing style that may indicate AI usage. By emphasizing the learning process over the final product, formative assessments can mitigate the impact of algorithmic bias and AI-generated content​(

TechNotes Blog

).

3. Implementing Oral Assessments and Presentations

Oral assessments and live presentations can provide an additional layer of authenticity. Asking students to verbally explain their thought process or present their findings can reveal their depth of understanding, making it difficult to rely on AI-generated content. This approach can be particularly effective in settings where verifying the originality of written assignments is challenging.

4. Exploring AI-Driven Assessment Tools for Detection and Insight

Paradoxically, AI can also be part of the solution. Tools like Turnitin’s AI writing detection or Grammarly’s similarity check can identify AI-generated content by analyzing patterns and discrepancies in writing style. While these tools are not foolproof, they offer a first line of defense against misuse of AI. Additionally, AI can provide insights into student performance patterns, helping to identify cases where algorithmic bias might be influencing student outcomes​(

European School Education Platform

).

5. Adapting Rubrics to Include Process-Based Criteria

Instead of focusing solely on the final product, rubrics should be adapted to assess process-based criteria such as research notes, drafts, and iterative improvements. This ensures that students are evaluated on the basis of their engagement and progression rather than just the end result, making it harder to submit AI-generated work without contributing original thought.

Addressing Algorithmic Bias in Student Performance

Algorithmic bias in AI tools can skew results and misrepresent student abilities, especially for underrepresented groups. For example, language models may produce biased or inaccurate outputs based on incomplete or biased training data. To address this, educators should:

1. Educate Students on Algorithmic Bias: Teach students to recognize and critique bias in AI outputs. This includes understanding how biases in language models can influence their work and learning to cross-reference AI-generated content with reliable sources.

2. Integrate Bias-Detection Tools: AI-powered bias-detection tools can be integrated into the assessment process to flag potentially biased content before it is used in student work. These tools can highlight problematic language or perspectives, making students more aware of the ethical considerations of using AI-generated content.

3. Foster Critical Thinking and AI Literacy: Emphasizing critical thinking and AI literacy is crucial in helping students navigate the use of AI tools. By fostering discussions around the limitations and ethical implications of AI, educators can empower students to use these technologies responsibly.

Conclusion

As AI continues to shape the educational landscape, adapting assessment practices to accommodate AI-generated work and mitigate algorithmic bias is essential. By emphasizing authentic, process-based, and oral assessments, utilizing formative strategies, and integrating AI literacy into the curriculum, educators can create a fair and effective assessment environment that maintains academic integrity and supports student growth.

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As before, when new techonologies arise and become available for our students, we face the co-habitant issue: what changes are needed in our way to teach to live with the newcome?

I'm more on embracing than fight the new tech, so I think we need to find a way to let students openly use AI gererative tools, discussing with them the limits we need to put in their use at school.

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AI is our reality and students use it, it is necessary to teach them to use it ethically, for example for ideas or quick tasks in class

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Adapting assessment practices to accommodate the shift in student learning approaches, including the use of AI-generated work, requires a multifaceted approach. To ensure fairness and effectiveness, educators can consider the following strategies:

Diversify Assessment Methods: Relying on a variety of assessment methods, such as oral presentations, interviews, and practical demonstrations, can help mitigate the impact of AI-generated work and reduce the potential for algorithmic bias to influence outcomes

1

Transparent Assessment Criteria: Clearly communicating assessment criteria to students can help them understand how their work will be evaluated, regardless of whether it is AI-generated or not. This transparency can also help mitigate the potential influence of algorithmic bias on students' work and performance

3

Formative Assessment: Implementing formative assessment strategies, such as providing ongoing feedback and opportunities for revision, can help address concerns related to algorithmic bias. By engaging in a continuous feedback loop, educators can identify and address any potential bias in students' work before it significantly impacts their performance

3

Algorithmic Bias Assessment: In addition to traditional assessment practices, educators can also consider conducting algorithmic bias assessments to evaluate the fairness of AI-generated work. This involves examining the underlying algorithms and data sources to identify and mitigate any potential biases that may impact the assessment process

4

By combining these strategies, educators can adapt their assessment practices to accommodate the evolving landscape of student learning while ensuring fairness and effectiveness.

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AI is here and now! There is no way to get around it. Despite our fear we must learn how to take advantage of its potential. It can help in formative assessment as it can give feedback instantly. Students must be taught how to use AI to improve their learning not just to get better results.

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It is important that we actively engage with artificial intelligence in education in order to prepare our students for the future. With regard to formative assessment, I can very well imagine using AI as a "tandem partner". Similar to medicine, where programmes evaluate patient results and then make possible treatment suggestions to doctors - doctors, on the other hand, do not blindly follow the suggestions, but adapt the suggestions individually to the patient. I can imagine something similar in education.

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I thought students in my subject wouldn't use AI anytime soon. Is it difficult to write mathematics on the computer and how will the machine help solve problems? After checking that they already use photomat or other tools, I have to prepare and study more to give a good answer to my student.

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AI tools seems to have revolutionized and are still affecting too much our society but the students should be aware of their pros and cons and the limits connected to their use in terms of fairness and equality.

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Formative assessment strategies can be employed to address concerns related to algorithmic bias influencing students' work and performance in several ways: Diverse Assessment Methods, Regular Reflection and Feedback, Critical Thinking Skills, Algorithm Transparency, Bias Detection and Mitigation, Human Oversight and Intervention, Ethical Considerations.

By incorporating these strategies into formative assessment practices, educators can create a more balanced and equitable learning environment, empowering students to critically engage with technology and navigate potential biases within AI-driven assessment tools.

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As a teacher, we cannot ignore the potential challenges and opportunities we face with the rise of AI and its impact on the development and delivery of assessments.

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Adapting assessment practices to align with the shift in student learning approaches driven by Artificial Intelligence involves several considerations to ensure fairness and effectiveness.

Personalized and Diverse Assessments:

Move away from traditional, one-size-fits-all assessments. Tailor assessments to accommodate diverse learning styles and preferences.

Utilize a mix of assessment types, such as project-based assessments, collaborative assignments, and real-world problem-solving tasks.

Continuous Formative Feedback:

Implement continuous formative assessment strategies to provide ongoing feedback, allowing students to understand their progress and make improvements.

Leverage technology for instant feedback through AI-powered tools, promoting timely and targeted interventions.

Transparency and Explainability:

Ensure transparency in the assessment process, providing clear expectations and criteria for evaluation.

Address algorithmic bias concerns by adopting assessment tools with transparent algorithms. Educate students on how AI is used in their assessment and provide explanations for outcomes.

Inclusive Design:

Design assessments that are inclusive and considerate of students with diverse backgrounds, learning needs, and abilities.

Regularly review assessment tools and practices for potential biases, adjusting them to promote fairness and equity.

Training and Awareness:

Provide training for educators on the use of AI in assessment and its potential impact on students.

Foster awareness among students about the presence of AI in assessments, promoting a better understanding of the technology and its role in their learning journey.

Ethical Considerations:

Establish clear ethical guidelines for the use of AI in assessments, ensuring the responsible and unbiased deployment of technology.

Regularly assess the ethical implications of AI-powered assessment tools and make adjustments as needed.

Flexibility and Accessibility:

Allow for flexibility in assessment formats to accommodate various learning environments, including in-person and remote settings.

Ensure that assessments are accessible to all students, considering factors such as language, disabilities, and technological access.

Continuous Evaluation and Improvement:

Continuously evaluate the effectiveness of AI-driven assessments and be open to modifying practices based on feedback and emerging best practices.

Encourage a culture of innovation and experimentation, fostering an environment where educators can adapt their approaches to meet evolving needs.

By incorporating these strategies, educators can harness the benefits of AI in assessments while addressing concerns related to fairness, bias, and effectiveness in student evaluation.

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it is good to use. But there are ethical issues

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As teachers, we can encourage research and learning with the help of artificial intelligence, but we must first of all teach students to think critically and solve problems. Students should discuss the information they received from AI and how to apply it. We should avoid summatively evaluating assignments that students do at home.

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Critical thinking, creativity are human characteristics that should be cultivated in school in order to address AI and biasm

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"I believe that students should be taught about the advantages and disadvantages of artificial intelligence. When evaluating, it's necessary to combine different approaches to assessment. AI should be included in education given the fact that it's all around us, but with caution and educating students about its capabilities. Continuous personal monitoring of students is certainly beneficial and will contribute to overall assessment."

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I think that to see and assess the real student critical and creative thinking, automatically with less possibilities of bias by the AI, we should make our tasks very specific. For assessment than we could use well defined rubrics, maybe made by ourselves, which would maybe provide a kind of the "critical audience"... Maybe :).

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I believe we should be learning partners with our students. It is much more effective to instil individual and teamwork in them than to prepare everything for them.

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We talked with students about ethics and communication on the Internet, I paid special attention to the discussion of artificial intelligence and cookies used on the Internet, we talked about how to cut off and why you need not to believe everything that is displayed in facebook ads.

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The student can use artificial intelligence as a tool to facilitate his work, but when presenting his task, the student will still have to reflect and explain the creative process himself.

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Students should know that AI serves to develop their critical thinking, not to think for them.

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I think it's acceptable for students to utilize generative AI tools, but it's crucial that they grasp the significance of these tools. For instance, after submitting an essay generated with such tools, they could be prompted with questions or asked to participate in a spontaneous debate. This approach ensures they pay close attention to the content they generate, leading to a more meaningful learning experience.

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As teachers we should modify our assessment strategies to focus more on creativity, critical thinking, and the application of knowledge rather than just factual recall. This shift can mitigate the impact of AI-generated content. We need to arrange open questions requiring students to apply, analyze, and synthesize information rather than just recall facts. This encourages deeper engagement with the material. i personally think that also I personally think the importance of transferring knowledge of the use of the artificial agent, how to adopt it, know its potential, limits and risks of epistemic vigilance: the Digital Literacy in order to educate students about algorithmic bias and the ethical use of AI tools. This empowers them to critically evaluate AI-generated content and understand its limitations.

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Beacause we live in an era of technology we have to use various formative assesments strategies as peer rewiews, qizzes , we should crittically assess, ensuring fairness and accuracy in evaluating, and providing additional support to counter any biases that may arise.

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Evaluation by AI can cause a lot of problems if there is an error in the process itself. Certainly, it would be positive to use it for formative evaluation in terms of assessing the progress of our students, in evaluation for learning and as learning.

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The only way is to call student's attention to the bias in AI. They should question what they read and analyse it in term of accuracy, truth and bias. To tell them that people are behind AI systems is essential. They need to understand that!

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Help students understand the potential issues associated with relying solely on AI-generated content and encourage critical thinking about information sources. Include project-based assessments, presentations, and collaborative assignments that emphasize practical application and critical thinking, reducing the likelihood of algorithmic bias influencing outcomes.

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To avoid cheating, teacher should change the way of giving tasks. For example, students can ask AI for literature, ideas, then critically discussed it in the classroom and then write essay in the classroom. Assessment should also be done in the classroom. Critical thinking is crucial when working with the AI so we will have to teach our students to be critical. After all, AI is algorithm made by man.

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As teachers who use AI in assessment, fairness could be achieved by using AI during class time.

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AI has potential challenges for student assessment. For example, it is important to ensure that AI tools are valid and reliable. It is also important for teachers to be aware of the limitations of AI and to use it in a way that is in the best interest of students.
 AI has the potential to significantly improve student assessment. As technology continues to evolve, we're likely to see even more ways AI can be used to improve grading.

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I agree that some students resort to AI- generated writing assignments such as essays, stories, etc. We might explain them it is a tool that should be used in their advantage not to make them less prepared when passing a test at school with a similar task as their homework. They have to understand, we the teachers are familiar with the latest technologies and we will do our best to assess them fairly and grade them correctly. Moreover they are our students and we see their performance during classes as well.

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It's tricky. But making students discuss, self and hetero-evaluating it's important. Explain that they always should zoom out before taking some conclusions.

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AI and ICT are in our lives, we want it or not. So teachers must adapt to technology, to using it and to ask students to be honest and tell when they used AI in their essays

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*

I. The student should argue or present his work orally.

II. In the STEM field, applying the steps of the scientific method reduces the algorithmic bias in education.

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The challenge of students using AI should be embraced as it is a good tool. However, we should empower our students t o analyse critically the responses generated in line with what is discussed in class and the textbook used.

A useful exercise that I think made all the difference in my class was to use it for the discussion of lab reports. A discussion was done in class on the possilbe outcome using ChatGPT for an exemplar experiment that was then regarded. Students had the time to argue the pros and cons and were shown how they could be inspired from what is generated without the need to copy and paste throughout so that they make it their own.

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I find AI chatbots like ChatGPT very useful to assess dull, repetitive assignments providing a rubric, anyway it needs a check by the teacher. If grades need a detailed answer, ChatGPT makes the magic for you. I find this like assessing a complicated quiz, having a mix of standard answers available. Of course you can also ask the chatbot if it generated the text to assess and to provide some bitchy remarks if this was the case :-D (have fun).

Instead, Art assignments show merciless any mental of physical health issue to the eye of any experienced teacher or psychologist, why AI would not detect that, too. I would not use it for Art assessment. But it's so useful to plan lessons and especially rubrics.

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I'm not sure to understand in which ways can the assessment be biased when using AI if it objectively analyse students output, but, maybe, we can use formative assessment itsefl as a way to provide ongoing feedback and guide students' learning throughout the process. This allows for early detection of potential issues and timely intervention.

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Formative assessments can be done in the classroom, where the teacher has full control of AI access. Likewise, the teacher should set multiple intelligence tasks so that there are areas that the AI can't complete or produce work at the moment.

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I think AI can be useful in creating the activities and assessing standardized exercises but writing, listening, reading comprehension activities and oral tests are important to learn languages and the teachers are the only one who can assess.

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Nowadays Ai is a new challenge for education. However the use of AI in the classroom should be monitored, so as the teacher can understand and asses students learning. An effective teacher must create a rapport  with their students, inspire them so that students acquire a set of values, such as critical  thinking to ensure that they will use AI  with fairness and  responsibility.

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Addressing Bias in (AI) Assessment

It is always a challenge to minimize bias in assessment, whether it is performed by a person or by AI. A middle way is probably the best solution, particularly in this early phase of AI development.

The concept of the flipped classroom is becoming more important - teachers should use the valuable time they have with their students in the classrooms (or other learning sites) to encourage face to face communication between students, concentrate on problem - solving tasks, provide valuable formative assessment face to face and present their students various learning methods, including AI assisted learning.

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Maybe AI should detect the type of taks it was asked to do, then compare the data about the person who ask for that, and in this way if the AI detects a student will not offer an essay but rather some general ideas and sources where the student can look in order to complete his task.

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We as educators need to open, through life-long learning, to AI and technology paths. Then, we should always talk to and actively listen to our students' approach to them, thus guiding them to critically think, re-think how they approach to AI and technology and fostering continuous feedback providing them with inclusive ones.

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AI can become either a blessing or a curse in our hands and in our students' hands depending on the use we will make of it. So, it's up to us to teach our students to critically use AI for their benefit and not only in order to get a good grade. Ethical issues arise concerning the use of AI too and we should find a balance for its use in order to get the most out of it.

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As educators, we're witnessing a shift in how students engage with learning. To ensure our assessments align with these changes and maintain fairness and effectiveness, a thoughtful strategy is crucial. Here's how I envision adapting our approach:

1. Embrace Diversity in Assessment:

Recognize and celebrate the diverse ways students learn by incorporating various assessment methods. This might include traditional exams alongside interactive projects, presentations, or online discussions.

2. Foster Continuous Feedback:

Cultivate a culture of ongoing learning through formative assessments. Regular feedback, whether from peers or self-assessment, becomes a valuable tool for student growth. Leveraging technology for efficient feedback delivery is key.

3. Flexibility is Key:

Acknowledge the different paces at which students learn by offering flexible deadlines and multiple attempts on assessments. This not only accommodates diverse learning schedules but also promotes resilience and improvement.

4. Real-World Problem Solving:

Shift the focus from memorization to real-world problem-solving skills. Encourage open-book or open-resource assessments to mirror the environments students will encounter beyond the classroom.

5. Inclusive Design Matters:

Design assessments with inclusivity in mind. Clear instructions and adherence to universal design principles ensure assessments are accessible to all students, regardless of their background or abilities.

6. Tackling Algorithmic Bias:

Stay vigilant about potential biases in automated grading tools. Regularly review and update algorithms to minimize bias and consider alternative assessment methods when necessary.

7. Transparency and Communication:

Foster an open line of communication with students. Clearly communicate assessment criteria, explain the purpose of assessments, and demystify any algorithmic tools used, ensuring students understand the evaluation process.

8. Continuous Educator Growth:

Invest in professional development opportunities for educators. Training on inclusive and bias-free assessment practices keeps us informed and ensures we're equipped to navigate the evolving educational landscape.

9. Regular Evaluation and Adaptation:

Continuously assess the effectiveness of our assessment methods. Student feedback and performance data will guide adjustments, and staying informed about emerging educational technologies is key.

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Tonka, Croatia                                                                                                                                

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Tonka, Croatia                                                                                                                                

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We must change the way we teach and learn. AI will always be better in analyzing and data management but AI is not smart in creative tasks and practice. We can use AI for evidence-based learning and simulation to motivate students and to develop practical skills.

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The fairness and effectiveness of any assessment practice are relative indicators. A person's assessment of another's achievements is subjective and may be biased. Of course, you can't rely 100% on AI, especially at the stage when it is still 'learning.' However, experience cannot be gained by doing nothing. Continuous analysis and reflection are challenging but possible paths.

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To be honest, I don't fully understand how the assessment done by AI tools could be biased, e.g., by the social background of students. When I use AI tools in my regular work as educator, I don't have to put information on students to the system. (and, because of the pivacy, GDPR, etc., I shouldn't). On the other hand, without any doubt, the AI tools are good provided that the input data were fine.

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Can someone provide more insight into the issue of algorithmic bias, underpriviledged students and AI exam assessement?

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Can someone provide more insight into the issue of algorithmic bias, underpriviledged students and AI exam assessement?

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AI and machine learning have the potential to greatly enhance education by providing personalized learning experiences and improving feedback loops. However, it's important to be aware of the potential risks of algorithmic bias and ensure that AI systems are designed and used ethically. By being proactive and responsible in their use of AI tools, educators can harness the benefits of AI while addressing concerns about bias and discrimination in education.

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The use of Artificial Intelligence in the evaluation process is a beneficial one, however in the given case the evaluation will be a very objective one. On the other hand, when a primary school student's work is evaluated by a human, then a ray of hope, kindness, encouragement can creep in. I believe that the little schoolboy must be evaluated from a human perspective.

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AI technology is helpful but at least for now it can't replace human's work, therefore teachers must always correct whatever work/assessment is done/created by it.

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We should utilize a variety of assessment formats, including projects, presentations, peer reviews, and practical applications, alongside traditional tests, combine traditional tests with projects, presentations, and practical tasks. This diversification can reduce the impact of AI on overall assessment, Assess students on analysis and originality, not just recall. Use frequent, low-stakes assessments for continuous insight.

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The use of AI technology in the classroom during assessment, I always introduce assessment rubrics and try to include all students in the discussion.

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A great part of our role as educators is to help our students prepare to face the outside world, so we should adapt to contemporary methods of education and learning. AI seems to be "the next big thing" and our students are fascinated, so we mustn't reject it without reason. On the contrary, we should cooperate with them in understanding and using AI tools.

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I don't think there can be any clear answers or solutions on how to navigate the impact of AI on assessment mostly in relation to algorithmic bias. As educators, our concern must be not only to use wisely all tools available, but also train our students to do the same. Apparently the era of traditional assessment methods has gone, which automatically calls for the need of looking at education and teaching/learning process from a different perspective. Students must understand the value of self reflection and self evaluation and teachers should use a variety of assessment methods not only to assess their students' learning and progress but also to evaluate their own teaching approaches. In my opinion it's totally our responsibility to take everything more seriously, especially now we're all aware that things might seem easier than before but in essence they're really a lot more demanding and users of AI tools must use them prudently and profoundly critically if they want to get fair results.

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To avoid biased AI assessment we need to try various methods and see what works best. Varied formative assessment techniques, combined with explanations by students on how they came up with solutions and not focusing on the end product of the assignment could be a solution to the cheating with the help of AI. Another could be generating in class AI assignments and adapting based on criteria.

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A continuous open discussion with students about how AI affects their work and when it is smart to use is essential. Students tend to use it to make their life easier but often don't check the AI provided work. To assess the actual student's work instead of assessing AI provided work, assessments should be conducted during lessons where teachers have a certain amount of control over the AI usage.

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We should RE-THINK assessment instead of just associating Ai with cheating. We have to learn and adapt to this new situation and try to embrace it. For that, we need to explore the possibilities first and ensure effectiveness in our new way of assessing.

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Receiving students' AI-generated assignments is inevitable, so our assessment practices need to change and adapt. One approach might be using formative assessment strategies to evaluate the process of task completion rather than the final product.

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Algorithms are built on data and data input relies on the people who are taking care of it. This leads to conclusion that any biases that occur are in fact not provided by the AI itself. As for assessment, a complete shift in this field is necessary, a wider range of tasks, clear instructions with lots of formative assessment. AI can be used as an assistant in this field by both the educators and the students.

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The growing concern about algorithmic bias in education is justified because there are several situations where it has been proven to occur. However, avoiding this bias is a challenging yet essential task to ensure the fairness and effectiveness of assessment practices.

In this perspective, educators need to adapt and flexibilize assessments to accommodate different learning styles, offering a greater variety of assessment formats to allow students to choose methods that best suit their abilities and preferences. Assessments must reflect a variety of perspectives and experiences to avoid cultural or social bias. It is advisable to incorporate diverse materials and examples to enhance fairness, regardless of students' backgrounds. The importance of competency-based assessment over memorization-based assessment should be emphasized. It is also crucial to involve students in the assessment process by participating in the definition of criteria and goals, as well as incorporating self-assessment procedures and self-regulation of learning, encouraging self-reflection and the development of metacognition.

Regarding concerns about algorithmic bias, we need to ensure algorithmic transparency in the formulation and application of assessments, clearly explaining how results are obtained. Human review is essential to mitigate potential algorithmic biases. It is also crucial for students to have the opportunity to contest and question assessment results.

We need to demand that companies providing AI certify their training with diverse and bias-free datasets, as well as ensuring constant monitoring of their algorithms and making continuous adjustments and improvements. Consideration of cultural and social implications in assessment metrics is also necessary to ensure they do not favor a specific group. Educational institutions need to establish ethical policies and practices for the use of algorithms in assessment. Educators, in turn, must create more inclusive, fair, and effective learning environments, adapting to changes in students' learning approaches.

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Include questions that prompt students to discuss their thought processes and reasoning, ensuring a deeper understanding of the material.

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Formative evaluation and self-evaluation of students should be carried out in every lesson. In this way, students should be responsible for their own knowledge and progress in their work as part of the learning process.

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We can use artificial intelligence to have our students generate original products: text to practice, generate images…. I am a Latin teacher, and in my classes, we work on translations. If I assign them a text to translate as homework, I'm sure they'll copy from ChatGPT. That's why we can do translations in class under my supervision. However, if we teach them to use ChatGPT to generate special texts for grammar practice with precise instructions, it can be very useful. For example, asking the image generator to create an image representing the translated text can be a lot of fun."

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AI systems are efficient only if the training data provided are good and possibly not biased. We should use them to promote critical thinking and we should always tell our pupils to think and reflect before assuming everything that comes for AI is genuine, real and with educational power. It's up to the educators to stimulate students to act responsibly. As regards the impact of AI in assessment we have to rethink our strategies according to this ever- changing educational environment

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In light of the continuous development of new AI tools each day, there's a growing awareness and effort to tackle Algorithmic Bias. I believe that as this momentum continues, these tools will undergo significant improvements, ultimately becoming more adept at avoiding biases. The ongoing creation and refinement of AI technologies will contribute to a more nuanced understanding and effective mitigation of algorithmic biases over time.

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Vasiliki, Greece

I couldn't agree more with Ivona. I firmly believe that students need to work on essays during school hours, otherwise when at home, the temptation to use AI will be irresistible, as it is for them now to copy from school curriculum-based solution books they can easily find, or making mom and dad to practically write for them. Otherwise, if they work at home , the presentation should be oral, so that even if they have "cheated", oral presentation means incorporating the benefits from their work. (It is like when we miniature- wrote on a tiny cheating paper "skonaki" to hide in our sleeve during the test, but then we realized that writing it down made us learn the topic by heart)

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AI systems are only as good as the data on which they are built. Humans input the data. If the data is poorly researched, unclear, or comes from unverified sources, it may contain racial, gender, or ideological biases. Therefore, bias may appear in the assessment. Clear, accurate, verified and unbiased data should be input into AI systems to reduce or eliminate bias. Therefore, it all depends on the person who inputs the data.

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In my view, when a teacher devises and later grades a test should not rely only on A.I. tools but also have in mind the particular classroom conditions and pupils the test addresses. So, he/she will make the changes he/she thinks and avoid possible algorithmic bias. In the case students "cheat" and produce A.I. generated essays, I think we should, on the one hand continue using essays as an assessment method, but on the other hand ensure (e.g. by asking them to comment and explain particular points of it) that it is their product.

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In my opinion, AI should be used in a way that promotes critical thinking. For example, we could ask AI to generate an essay and then provide the generated text to our students, asking them to express their opinions on whether they agree or disagree with the generated essay.

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As every drasticly new thing in our lives so AI will find place in our hearts and minds, we just have to embrace it and use it wisely.

I am not afraid, I am pleasently surprised with unexpected possibilities of AI as colaborator, help, learning platform... Use of AI will force education system, and its carriers, to move emphasies from learning facts towards critical thinking, problem solving, social skills... everything that human mind is, still :), are better than AI.

I am math teacher and it is obvious that aproach towards teaching math should move from procedures to understanding, from calculus itself towards problem solving, learning based on researches, critical thinking and so on. The first consequence that I see, for some time, is useles of homework as individual student work in shape that we use in presence. Teacher must change their outcomes and goals in teaching process.

As far as assessment in classroom, techers regular monitoring and data that he collect can improve student learning, and AI can be welcomed just as help but not substitute.

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I honestly fail to see how algorhitmic bias can influence classroom practices, short of having an autonomous AI carrying out assesements without any input from a teacher. It might hapen in the future, but for now, using chatGPT to create a rubric or lesson plan doesn't leave much room for bias.

As for adapting our assesement practices to students' use of generative AI, some strategies are: asking students to explain some ideas in their text, or assigning references that we are familiar with and students have to use as a base for their text.

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I think essays and projects should be designed as comprehensive tasks as defined by the CEFR; I mean, giving the same instructions to students so that they can apply design and divergent thinking to produce different results according to their interest and needs. We also need to think of tasks in which they apply their knowledge (as if they had the chance of doing the tasks with their books). AI should be a tool the same as they use translators and dictionaries, but they should refine products. As some colleagues say, teachers should correct such tasks to avoid algorithm biases.

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The shift towards AI-generated content in education poses challenges for traditional assessment methods. To adapt and ensure fairness and effectiveness, educators can consider several strategies. First, emphasizing critical thinking and creativity in assignments can provide a space where AI struggles to replicate human capabilities. Tasks involving problem-solving or open-ended questions that demand original thought can be valuable. Second, focusing on the process of learning rather than just the final product can reveal insights into students' understanding and effort. Assignments that require documentation of thought processes, research methods, and decision-making contribute to a holistic assessment.

In addition to these approaches, integrating human feedback into the assessment process is crucial. While AI is a valuable tool, the nuanced understanding and subjective insights that humans bring are essential. Peer reviews, teacher assessments, and constructive feedback can add a human touch to evaluations. Teaching digital literacy and ethics is another key aspect. Equipping students with the skills to critically evaluate AI-generated content and understanding the potential biases in algorithms fosters a more informed use of technology for academic purposes.

Diversifying assessment methods is also recommended. Instead of solely relying on written assignments, incorporating oral presentations, group projects, and other forms of assessment that may be less susceptible to AI manipulation contributes to a comprehensive evaluation.

Regarding formative assessment strategies, regular check-ins with students provide insights into their understanding, progress, and challenges. This ongoing dialogue enables educators to identify issues early on and offer timely support. Peer collaboration is another effective strategy, fostering a collaborative environment where students can work together on assignments, promoting a sense of community and peer learning.

Exploring adaptive learning platforms that provide personalized feedback based on individual student performance is also beneficial. These platforms can adapt to each student's learning pace and style, enhancing the effectiveness of formative assessment. Additionally, incorporating reflective practices where students assess their own work, set goals for improvement, and track their progress over time encourages metacognition and a deeper understanding of their learning journey. By implementing these strategies, educators can navigate the challenges posed by AI-generated content and algorithmic bias, promoting fair and effective assessment practices in the evolving landscape of education.

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Addressing these algorithmic issues requires ongoing research and development in artificial intelligence, natural language processing, and machine learning, so as for now we must evaluate with the same method as we did before AI

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I think we can rely on AI tools in multiple choice, true-false or short answer type tasks. Essay works should be corrected by teachers.

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Algorithms are created using data, and the data used depends on the people responsible for managing it. This means that any biases that arise are not actually caused by the AI itself. In terms of assessment, there needs to be a significant change in this area, including a broader range of tasks, clear instructions, and frequent feedback. AI can serve as an assistant to both educators and students in this field. Our task, as educators, is to be aware of the possibilities of inequality and to teach our students that AI is created by humans and is not always impartial.

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By embracing a balanced approach that combines traditional assessment practices with the integration of AI tools while prioritizing fairness, transparency, and flexibility, educators can create an assessment framework that accommodates diverse learning approaches while ensuring effective evaluation of student learning.

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I continue to believe that it is not necessarily ethical to use artificial intelligence in all situations, such as a songwriting project. However, in other situations, addressing the impact of AI on assessment requires rethinking, ensuring equity, efficiency and adaptability in an ever-changing educational environment.

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Although AI is becoming innevitable in school surroundings, I believe that we should remain faithful to our oral examinations, and maybe project based work done at school, not at home. As I work in an elementary school, I have witnessed many projects, done at home, even without AI, which were done by "AI mum"/dad/aunt ... so my solution was to adapt in a way that I did not grade the project itself but the pupilsˋ speaking and presentation skills. Later on I would give them projects which were done at school, so there would be no bias, each one of them would show their own knowledge. I agree with some that are suggesting more teachersˋ education on the subject, because we have to adapt to the new circumstances and be prepared to know the difference between our studentsˋ knowledge and AI help.

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We can embrace AI because it is already here, and it is not going away, but rather it will only get better. The only way I see formative assessment being objective is if it is done in the school. Children belong in school, don't they?

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As a non-teaching staff I'm not in the situation to evaluate students' assessment. My focus is on developing students' skills and competencies for lifelong learning. In this process AI can be very powerful tool, but it's important that students are aware that AI has it's own advantages and challenges. Therefore my effort in addressing bias would be in emphasizing the importance of checking out the credibility of information given by AI.

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I cannot evaluate work done without direct oversight. If I want to conduct formative assessment, I can explicitly ask students to have an AI develop the test, and then, after informing them about the bias issue, ask them to take on the roles of different subjects, disadvantaged and not, and critically analyze the work produced by the AI, possibly with a class discussion. This is not applicable to all disciplines; indeed, if they have to solve an equation, it is difficult to find different roles

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Humans and their interests are responsible, not computers

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Well, I had never thought about AI bias before watching the video. I agree with those who say that avoiding using it is not the answer. We must learn to use it and teach our students how to use it properly. I found the initial activity very interesting, i.e. writing a presentation by using AI and adding to it more personal details/info to make it more 'human' and not just 'artificial'. So as other colleagues on this course have suggested, probably creativity is a possible answer as well as developing critical thinking in order to let students realize both the positive and negative aspects of this incredible tool.

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Students should be aware of this and even the teachers. So as Educators, we should teach them about AI bias. It's essential that they learn critical thinking because what we are experiencing now it is the beginning of the era of AI.

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It is now more important than ever that our learners are equipped with the skills to help them analyse their work and the information they access and also to make use of their gained knowledge effectively so that they may truly show understanding and mastery of it in varied ways. Formative assessment and assessment for learning pedagogies have always strived to focus on these learning to learn skills by enabling learners to take more responsibility of their learning, learning how to self-assess their learning and by helping them make more sense of what they are learning. In order to mitigate issues related to AI and algorithmic bias, it becomes more necessary that students are able to show understanding of new knowledge through varied modes and not just through traditional assessments, such as essays. Learners should be assessed on their ability to represent learnt knowledge, to apply learnt knowledge in a situation, to problem-solve using learnt knowledge and to create artefacts or communicate using learnt knowledge. This means that in order to counteract the issues mentioned here, educators should make sure that students are involved in the process of assessment by being involved in constructing criteria for success which will be used to determine their performance. Learners should be made aware of certain issues and pitfalls of using AI in assessments, and how these issues can be addressed for fairness and reliability issues by having a common rubric which outlines descriptors of what a good performance entails.

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Today more than ever there is talk about AI, coding, programming, algorithms, etc., so it is a challenge both for the students who have to learn the latest science and for the teachers how to include them in the lessons, different projects and to be as accurate as possible in our assessments, despite the problems that appear in the background...

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The issue related to using AI in education is really challenging and thought provoking. I think we should take advantage of its benefits (ideas for activities, for strategies of teaching etc), but be aware of its drawbacks and the impact it can have on students.

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Avoiding superficial use of AI is something that students should be taught and teachers trained. I prefer creative tasks to assess my students and give them the opportunity to produce something original and individual.

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As I'm working with kindergarden children, I find it rather difficult to express my opinion about AI and algorithmic bias. We have to find with our older students the prons and cons of AI in order to use it carefully.

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AI bias can not be avoided. We need to think wisely about whether we can more easily develop critical thinking by solving practical problems or by using AI that will do it for us and our students. Perhaps AI should be maximally avoided in education at lower educational levels, and students should be individually observed, taught with guidance, warm feedback, mentoring support. I wish all of us here were shaping attitudes that would steer education in a more humane direction

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In the context of utilizing AI in education, it's essential to engage in discussions of its ethical use, provide students with skills required for the digital age, and demonstrate responsible AI use. We should also include our students in the decision-making process and collaborate with all the parties involved. Continuous professional development and ongoing evaluation, ensuring transparency, are pivotal.

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Expanding critical thinkiig should be the aim when using AI. Young students should be taught skills and not to use AI in their tests when they take a course. Students should make their effort to learn and not to outsmart the educational system.

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Formative assessment strategies applied throughout a course of pedagogical action can help – student and teacher – to reach the level of development closest to the student’s real state of knowledge. Thus, this will exercise a more critical potential, it will better define its path in the face of situations that allow it to remediate/consolidate knowledge and progress towards success.

In this particular situation, the algorithm (AI) can be compared with real human assessments, which make it possible to see the student's level of development without additional questions other than evaluating knowledge or evaluating it in a specific area of knowledge.

In other words, we can always take advantage of proposals for activities and tasks, but always in interaction with the teacher's perspective as mediator. Ai is an educational challenge.

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We should consider project-based assessments, presentations, and case studies that showcase practical skills and understanding, include a mix of written assignments, oral assessments, practical exams, and collaborative projects. and offer many opportunities for practice so when students are supposed to take an exam, they don't feel daunted and thus are less likely to cheat.

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Students should be aware that AI is just a tool which must carefully be worked on. It has a lot of benefits when used properly.

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Using artificial intelligence, we aim to teach students critical thinking skills, and perhaps tasks involving open-ended questions or requiring drawing conclusions from given data would be the best assessments. In this context, emphasis would be placed on evaluating creativity and the presentation of students' work.

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In my view, technology and AI is not something we can rule out and we want our students to make the best out of it. As teachers we wouln't like to be deprived of these tools so we should adapt our assessment methods to this significant shift. Let's take a step ahead of our students!! Consider this scenario: students have generated an essay using AI. Let's ask them to substatiate their words/opinions/statements. How about requiring that they start a debate counteracting different positions on given topics? Let's push them to put to good use the knowledge they have gained. There's no other option but to go with the flow