Teacher agency in the age of generative AI: towards a framework of hybrid intelligence for learning design
- URL: http://arxiv.org/abs/2407.06655v1
- Date: Tue, 9 Jul 2024 08:28:05 GMT
- Title: Teacher agency in the age of generative AI: towards a framework of hybrid intelligence for learning design
- Authors: Thomas B Frøsig, Margarida Romero,
- Abstract summary: Generative AI (genAI) is being used in education for different purposes.
From the teachers' perspective, genAI can support activities such as learning design.
However, GenAI has the potential to negatively affect professional agency due to teachers' limited power.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Generative AI (genAI) is being used in education for different purposes. From the teachers' perspective, genAI can support activities such as learning design. However, there is a need to study the impact of genAI on the teachers' agency. While GenAI can support certain processes of idea generation and co-creation, GenAI has the potential to negatively affect professional agency due to teachers' limited power to (i) act, (ii) affect matters, and (iii) make decisions or choices, as well as the possibility to (iv) take a stance. Agency is identified in the learning sciences studies as being one of the factors in teachers' ability to trust AI. This paper aims to introduce a dual perspective. First, educational technology, as opposed to other computer-mediated communication (CMC) tools, has two distinctly different user groups and different user needs, in the form of learners and teachers, to cater for. Second, the design of educational technology often prioritises learner agency and engagement, thereby limiting the opportunities for teachers to influence the technology and take action. This study aims to analyse the way GenAI is influencing teachers' agency. After identifying the current limits of GenAI, a solution based on the combination of human intelligence and artificial intelligence through a hybrid intelligence approach is proposed. This combination opens up the discussion of a collaboration between teacher and genAI being able to open up new practices in learning design in which they HI support the extension of the teachers' activity.
Related papers
- Transforming Teachers' Roles and Agencies in the Era of Generative AI: Perceptions, Acceptance, Knowledge, and Practices [0.7416846035207727]
This paper explores the transformative impact of Generative Artificial Intelligence (GenAI) on teachers' roles and agencies in education.
It presents a comprehensive framework that addresses teachers' perceptions, knowledge, acceptance, and practices of GenAI.
arXiv Detail & Related papers (2024-10-03T21:59:01Z) - Model-based Maintenance and Evolution with GenAI: A Look into the Future [47.93555901495955]
We argue that Generative Artificial Intelligence (GenAI) can be used as a means to address the limitations of Model-Based Engineering (MBM&E)
We propose that GenAI can be used in MBM&E for: reducing engineers' learning curve, maximizing efficiency with recommendations, or serving as a reasoning tool to understand domain problems.
arXiv Detail & Related papers (2024-07-09T23:13:26Z) - The AI Assessment Scale (AIAS): A Framework for Ethical Integration of Generative AI in Educational Assessment [0.0]
We outline a practical, simple, and sufficiently comprehensive tool to allow for the integration of GenAI tools into educational assessment.
The AI Assessment Scale (AIAS) empowers educators to select the appropriate level of GenAI usage in assessments.
By adopting a practical, flexible approach, the AIAS can form a much-needed starting point to address the current uncertainty and anxiety regarding GenAI in education.
arXiv Detail & Related papers (2023-12-12T09:08:36Z) - Innovating Computer Programming Pedagogy: The AI-Lab Framework for
Generative AI Adoption [0.0]
We introduce "AI-Lab," a framework for guiding students in effectively leveraging GenAI within core programming courses.
By identifying and rectifying GenAI's errors, students enrich their learning process.
For educators, AI-Lab provides mechanisms to explore students' perceptions of GenAI's role in their learning experience.
arXiv Detail & Related papers (2023-08-23T17:20:37Z) - The AI generation gap: Are Gen Z students more interested in adopting
generative AI such as ChatGPT in teaching and learning than their Gen X and
Millennial Generation teachers? [0.0]
Gen Z students were generally optimistic about the potential benefits of generative AI (GenAI)
Gen X and Gen Y teachers expressed heightened concerns about overreliance, ethical and pedagogical implications.
arXiv Detail & Related papers (2023-05-04T14:42:06Z) - Students' Voices on Generative AI: Perceptions, Benefits, and Challenges
in Higher Education [2.0711789781518752]
This study explores university students' perceptions of generative AI (GenAI) technologies, such as ChatGPT, in higher education.
Students recognized the potential for personalized learning support, writing and brainstorming assistance, and research and analysis capabilities.
Concerns about accuracy, privacy, ethical issues, and the impact on personal development, career prospects, and societal values were also expressed.
arXiv Detail & Related papers (2023-04-29T15:53:38Z) - Cybertrust: From Explainable to Actionable and Interpretable AI (AI2) [58.981120701284816]
Actionable and Interpretable AI (AI2) will incorporate explicit quantifications and visualizations of user confidence in AI recommendations.
It will allow examining and testing of AI system predictions to establish a basis for trust in the systems' decision making.
arXiv Detail & Related papers (2022-01-26T18:53:09Z) - Building Bridges: Generative Artworks to Explore AI Ethics [56.058588908294446]
In recent years, there has been an increased emphasis on understanding and mitigating adverse impacts of artificial intelligence (AI) technologies on society.
A significant challenge in the design of ethical AI systems is that there are multiple stakeholders in the AI pipeline, each with their own set of constraints and interests.
This position paper outlines some potential ways in which generative artworks can play this role by serving as accessible and powerful educational tools.
arXiv Detail & Related papers (2021-06-25T22:31:55Z) - Creation and Evaluation of a Pre-tertiary Artificial Intelligence (AI)
Curriculum [58.86139968005518]
The Chinese University of Hong Kong (CUHK)-Jockey Club AI for the Future Project (AI4Future) co-created an AI curriculum for pre-tertiary education.
A team of 14 professors with expertise in engineering and education collaborated with 17 principals and teachers from 6 secondary schools to co-create the curriculum.
The co-creation process generated a variety of resources which enhanced the teachers knowledge in AI, as well as fostered teachers autonomy in bringing the subject matter into their classrooms.
arXiv Detail & Related papers (2021-01-19T11:26:19Z) - Distributed and Democratized Learning: Philosophy and Research
Challenges [80.39805582015133]
We propose a novel design philosophy called democratized learning (Dem-AI)
Inspired by the societal groups of humans, the specialized groups of learning agents in the proposed Dem-AI system are self-organized in a hierarchical structure to collectively perform learning tasks more efficiently.
We present a reference design as a guideline to realize future Dem-AI systems, inspired by various interdisciplinary fields.
arXiv Detail & Related papers (2020-03-18T08:45:10Z) - Explainable Active Learning (XAL): An Empirical Study of How Local
Explanations Impact Annotator Experience [76.9910678786031]
We propose a novel paradigm of explainable active learning (XAL), by introducing techniques from the recently surging field of explainable AI (XAI) into an Active Learning setting.
Our study shows benefits of AI explanation as interfaces for machine teaching--supporting trust calibration and enabling rich forms of teaching feedback, and potential drawbacks--anchoring effect with the model judgment and cognitive workload.
arXiv Detail & Related papers (2020-01-24T22:52:18Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.