Aug 2026· Journal of Education and Learning Environments· Vol 2, pp. 45–55· 0 citations· 41 references
TL;DR
A context is established in which reflective classroom surveys can help teachers implement process-based grading structures to empower students in their growth as authentic learners instead of defaulting to passive users of AI.
Abstract
The rapid saturation of research on Artificial Intelligence (AI) in the classroom reflects concerns with the even more rapidly developing AI in many aspects of society. Framing the parameters of AI literacy can serve as an important context within which educators can guide students toward critical engagement with AI tools in order to develop their self-directed learning. Before this can happen, however, an aspect to consider in order to harness the potential of AI tools includes the necessity of aligning grading practices with self-directed learning. This article explores how such an alignment may foster a learning environment that shifts the use of AI away from merely generating substitutive text and toward actually harnessing its potential as a tool for helping authentic learning to happen. Drawing on relevant research and two survey responses from secondary students, this article aims to establish a context in which reflective classroom surveys can help teachers implement process-based grading structures to empower students in their growth as authentic learners instead of defaulting to passive users of AI. In such a context, the secondary classroom would remove the barriers created by hierarchical grading practices, evolve with emergent technology, train students to interact meaningfully and ethically with it, and ultimately prepare them for the ongoing challenges of using AI in the humanities and society, overall.
This paper examines the opportunities and risks associated with student-facing conversational artificial intelligence (AI) in primary education. It aims to evaluate how large language models (LLMs) can support personalised learning while identifying developmental, pedagogical and ethical challenges. Rather than treating benefits and risks as discrete factors, the study conceptualises AI as a socio-technical intervention that reshapes relationships between learners, teachers and knowledge.
The paper adopts a conceptual and theory-driven approach, synthesising current literature on AI in education, pedagogical theories and emerging practices in primary classrooms. The analysis is structured through a tension-oriented synthesis, identifying points of alignment and misalignment between AI affordances and core learning processes in primary classrooms. Based on this synthesis, the study develops a set of guiding principles grounded in developmental and educational considerations.
Conversational AI offers significant benefits, including personalised learning support, immediate feedback and reduced teacher workload. However, risks include cognitive offloading, overreliance on AI, misalignment with curriculum goals and ethical concerns such as bias and privacy. The analysis suggests that these are not independent challenges but reflect underlying tensions between technological capabilities and pedagogical requirements.
The study is conceptual and lacks empirical validation. Future research should focus on longitudinal and classroom-based studies to assess the actual impact of AI on primary learners' cognitive and social development. The paper highlights the need for interdisciplinary research bridging education, AI and developmental psychology.
The study proposes a set of guiding principles derived from the identified tensions, emphasising teacher-mediated interaction, developmental calibration of AI use, transparency, curriculum alignment, privacy protection and equity considerations. These principles provide a structured basis for integrating AI in ways that support learning processes while mitigating potential risks.
The adoption of AI in primary education raises concerns about equity, access and digital divides. Without careful implementation, AI may reinforce existing inequalities. Promoting critical AI literacy and ethical awareness among young learners is essential to prepare them for responsible participation in an AI-driven society.
This paper contributes a developmentally informed, tension-based conceptual framework for understanding student-facing AI in primary education. By reframing commonly identified opportunities and risks as interrelated tensions, it offers a more analytically grounded basis for guiding AI integration beyond descriptive or normative approaches.
With the recent rise of so-called “generative AI,” instructors have struggled with how to motivate students and ensure they are not relying on gen AI to produce work when its use interferes with the development and achievement of the skill-based learning objectives (SLO) of the course. Part of the problem arises when the students prioritize grades rather than skills as the most meaningful outcome for the course. One of the ways to encourage students to invest in achieving the SLOs is to move from the directed teaching style of pedagogy towards andragogy, where students self-direct themselves and their learning. In this paper, I will introduce a framework for course design that uses the principles of andragogy to create an assessment system I have dubbed “gamified grading.” By giving students a degree of agency over how to practice the outcome skills to earn their grade, they are able to “play” the course in a way that is most engaging and meaningful for them. This promotes students with diverse motivations and learning styles to become invested in achieving the SLOs without relying on generative AI.
Kurtis Hanlon· Proceedings of the H-Net Tea...· 0 citations
This article re-examines the role of assessment within the rapidly evolving landscape of artificial intelligence (AI), focusing specifically on differentiated instruction, and highlights the potential of AI to not only streamline the assessment process but also cultivate a more equitable and student-centered learning environment.
Hoai Thu Trinh· VNU Journal of Foreign Studi...· 0 citations
It is argued that students' needs matter on their own, the field should start from what students need when deciding how to use AI in design education, and good educational frameworks should be anchored in the learner, not driven by technology.
Providing feedback to students is an important yet time-consuming part of teachers’ work. Advances in AI technologies, especially generative AI, have introduced innovative solutions to automate the feedback process. The use of AI for feedback has generated interest in higher education, mainly attributed to its potential to reduce teachers’ workload and enhance feedback timeliness in response to the massification of university education. However, there remains a gap in empirical research that focuses on the ethical implications of this practice. In view of this, the paper draws on a diverse dataset (i.e., university policy reviews, social media posts and interviews with university teachers and students) to extrapolate eight key areas of ethical considerations regarding teachers’ use of AI for feedback purposes. Building on these areas, we call for a more nuanced understanding of what it means for teachers to use AI for feedback, considering various contextual complexities such as the purposes of assessment, the features of student assignments, and the types of feedback automated by AI. The study also highlights the need to move beyond the binary question of whether teachers should use AI or not towards exploring how feedback activities and teacher AI use could be designed and operated in ways that maintain and even enhance care, trust, and human connections central to effective feedback processes. While the study focuses on teachers, promoting the ethical use of AI requires a collective effort from multiple stakeholders in and beyond higher education.
Jiahui Luo, S. Eaton· Journal of University Teachi...· 0 citations
Artificial intelligence (AI) has the potential to enhance learning and increase student participation, yet its incorporation in teaching depends heavily on teachers’ perceptions. This study draws on a survey of 285 teachers to explore their perspectives on the use of AI in education for students with special educational needs. Using statistical analyses and summative content analyses, the results revealed that 15.8% of teachers reported using AI in their schools, while only 7.4% felt confident in their ability to use it. Although current use is limited and familiarity with AI remains low, teachers acknowledge its potential to support student learning and enable innovative teaching practices. However, they also express concerns about equity and the credibility of AI tools, indicating a hesitation to fully embrace AI in the classroom. These findings highlight the importance of targeted professional development to equip teachers with the competencies needed to incorporate AI into their practice.
Annika Käck, Helena Hemmingsson, Joacim Ramberg et al.· Journal of Special Education...· 0 citations
