Jun 2026· Journal of Ethics in Higher Education· 0 citations· 16 references
TL;DR
Assessment in Tanzanian higher learning institutions was examined and it was revealed that students integrate AI tools as cognitive extensions, enhancing understanding, argumentation and metacognitive reflection, while lecturers noted benefits alongside challenges in evaluating independent learning.
Abstract
The rapid integration of artificial intelligence (AI) technologies in higher education is transforming teaching, learning, and assessment practices. Generative AI systems, such as ChatGPT and Google Gemini, enable students to generate ideas, summarize academic materials, and refine written work, challenging the validity of conventional assessments designed to measure independent intellectual effort. Despite this potential, limited research has explored how assessment can be reconceptualized to accommodate AI-assisted learning while maintaining academic rigor. This study addresses the question, how student assessment can be aligned with AI-mediated learning in higher education. Guided by Extended Mind Theory, which conceptualizes cognition as distributed across human and technological agents, the study examined assessment in Tanzanian higher learning institutions. Using a qualitative multiple case study design, data were collected from 68 participants through interviews, focus groups, open-ended questionnaires and document reviews. Thematic analysis revealed that students integrate AI tools as cognitive extensions, enhancing understanding, argumentation and metacognitive reflection, while lecturers noted benefits alongside challenges in evaluating independent learning. Emerging approaches including oral presentations, reflective journals, project-based tasks and AI-transparent reporting were identified as effective. The findings underscore the need for AI-inclusive assessment frameworks emphasizing process, reflection and critical engagement.
This article re-examines the role of assessment within the rapidly evolving landscape of artificial intelligence (AI), focusing specifically on differentiated instruction. As AI tools become more integrated into education, traditional, one-size-fits-all assessment methods are proving to be inadequate for capturing the full scope of student learning. This paper argues for a paradigm shift, urging educators to rethink assessment by embracing a differentiated approach where students can demonstrate their mastery through diverse and creative products. We explore how AI can serve as a powerful ally in this process, offering new ways to automate feedback, analyze complex student outputs, and provide personalized support that goes beyond conventional grading. However, this shift also presents significant challenges, including concerns about academic integrity and the ethical use of AI. By reviewing current literature and proposing a forward-looking framework, this paper 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
The rapid integration of generative Artificial Intelligence (AI) into higher education writing instruction is outpacing pedagogical frameworks, creating profound disruptions in how writing is taught, assessed, and valued. While AI shifts writing from individual production to collaborative human–AI processes, instructors face escalating challenges, including the erosion of traditional authorship, uncertainty in evaluating AI-mediated work, threats to assessment validity, and growing student dependency on AI tools. These tensions expose a widening gap between technological adoption and pedagogical preparedness, placing faculty at the center of unresolved ethical, instructional, and institutional dilemmas. This systematic review synthesizes empirical research published between 2023 and 2025 on generative AI (e.g., ChatGPTand other GPT-based systems) in higher education writing instruction. Following PRISMA guidelines and SPIDER framework, 19 peer-reviewed studies were analyzed. Findings suggest that AI can enhance drafting, revision, and feedback processes, improving coherence, metacognition, and writing confidence. However, these benefits are accompanied by persistent concerns regarding ethical ambiguity, inconsistent policy guidance, and insufficient faculty training. The review contributes theoretically by reconceptualizing writing pedagogy for AI-mediated processes and practically by providing guidance for instructional design, assessment strategies, and institutional policy. Gaps identified include a lack of longitudinal studies, limited exploration of faculty perspectives, and inconsistent integration of AI literacy and ethical considerations. Implications for research, practice, and policy are discussed.
Samira Dichari, Fadi Jaber· Journal of Education and Tra...· 0 citations
Generative artificial intelligence (AI) is increasingly shaping how university students search for information, write, create, communicate, and solve problems. In higher education, this situation requires not only operational skills for using AI tools, but also broader competencies such as critical evaluation, information literacy, ethical judgment, self-regulation, and collaborative reflection. This study examines a classroom practice using Personary, a digital mind-mapping platform with an optional AI-assisted mode, to explore how university students conceptualize competencies needed in the AI era. The activity was conducted at two Japanese universities. Students received a common instructional presentation on digital safety, misinformation, AI risks and benefits, cognitive bias, and digital well-being. They then discussed the question, “What competencies should university students develop in the AI era?” and created collaborative mind maps using Personary. Student-generated mind maps and written reflections were analyzed through interpretive map analysis and text-mining-assisted qualitative analysis. The results show that students understood AI-era competencies as multidimensional capacities rather than as technical skills alone. Their maps and reflections emphasized critical evaluation of AI-generated information, media and data literacy, autonomous thinking, communication, ethical responsibility, appropriate AI use, and adaptability. Personary supported the externalization and organization of these ideas, while the AI-assisted mode provided additional prompts for expanding selected branches. The study demonstrates how AI-supported mind mapping can function as a reflective learning activity for visualizing, sharing, and reorganizing students’ understanding of AI literacy in higher education.
University students belonging to Generation Z have brought to higher education learning habits, attention patterns, and expectations formed almost exclusively in an environment where interactions with digital and algorithmically mediated technologies prevail. Simultaneously, advanced generative and adaptive artificial intelligence (AI) tools are no longer limited to peripheral applications within the field of educational technology but have become integral in helping students search for information, develop writing skills, receive constructive feedback, and manage their learning behavior. This paper offers a conceptual and systematic literature review of the topic of how AI could be effectively used in higher education in order to improve learning of Generation Z students. The study analyses peer-reviewed research articles found in Scopus- and Web of Science-indexed Journals over the period of 2015-2026 and focuses on the ways how personalized and adaptive learning systems, intelligent tutoring systems, and generative AI tools like ChatGPT influence students' motivation, engagement, and self-regulated learning. In order to do so, the review employs four theoretical frameworks: Prensky's framework of digital natives, Self-Determination Theory, Self-Regulated Learning Theory, and the Unified Theory of Acceptance and Use of Technology (UTAUT2). The paper discusses the existing pedagogical approaches such as flipped and hybrid classrooms, use of AI in formative assessment, gamification and project-based learning, and prompt literacy, as well as major problems associated with the application of AI in the learning process (algorithmic bias, academic integrity, dependency on AI, data privacy issues, and inequitable access). In the conclusion, AI has the highest potential in education when it is used to complement rather than to replace human instruction, and when universities combine technology use with training in critical thinking, digital ethics, and self-control. Implications for curriculum design, teacher development, and policy-making are discussed along with suggestions for future research.
Sunny Seth, Dr Disha Grover· Journal of Asia Entrepreneur...· 0 citations
This study examines undergraduate Information and Communications Technology (ICT) students’ needs, expectations, and concerns regarding Artificial Intelligence (AI)-supported adaptive learning for programming education in Kazakhstan. Using a mixed-methods descriptive design, data were collected from 134 students at two universities via a multilingual online questionnaire that combined Likert-scale items with open-ended questions. Quantitative results indicate high awareness of AI and strong readiness to use AI-supported learning tools, with the highest-rated needs focusing on adaptive content, automated practice/task generation, progress tracking, and mobile access. At the same time, students reported moderate concerns about the accuracy of AI-generated materials and the privacy of learning data, and they preferred models where teachers remain actively involved in interpreting and guiding AI feedback. Qualitative findings reinforced these patterns, identifying technical reliability, language/localization, transparency, and integration with existing Learning Management System (LMS) as key barriers, while emphasizing stability, explainable feedback, multilingual support, and teacher-in-the-loop functionality as priorities for improvement. This study should be interpreted as a pre-implementation needs assessment based on students’ responses to a hypothetical AI-supported learning scenario and does not provide evidence of actual adoption or instructional effectiveness. The study contributes user-derived design requirements that can guide the development of trustworthy and context-appropriate AI-supported learning platforms for undergraduate ICT students in programming-related courses at the two participating universities; broader generalization to other higher education fields requires further research.
Кazimova Dinara, Turmuratova Dinara, Zatyneyko Anatoly et al.· International Journal of Inf...· 0 citations
The rapid growth of generative Artificial Intelligence (AI) is reshaping how higher education conceptualizes learning, assessment, and pedagogy. Many institutions respond by relying on restrictive policies. Unfortunately, this approach often fails to support meaningful and sustainable education. The objective of this study is to reinterpret the Artificial Intelligence Assessment Scale (AIAS) (Perkins et al., 2024) as a developmental pedagogical framework that enables transparent, ethical, and reflective integration of AI into teaching and learning. Methodologically, the study applies the five-level AIAS model, ranging from AI prohibition to full AI collaboration, in FASH 137: Clothing, Society, and Culture, a General Education course examining the sociocultural meanings of dress. AI integration is scaffolded across multiple assignments, each explicitly aligned with a designated AIAS level. Data are drawn from assignment design, faculty observations, and structured student reflections documenting AI use and learning outcomes. Findings reveal three interrelated pedagogical themes. First, transparency as pedagogical integrity emerges through required “AI Use Notes,” which normalize disclosure and foster academic trust. Second, critical evaluation as human distinction is strengthened as students compare AI-generated insights with their own analyses, reinforcing judgment, creativity, and cultural interpretation. Third, AI as a structured learning partner supports exploration, critique, writing development, and identity reflection without replacing human authorship. The research outcomes demonstrate that AIAS functions effectively as a learning architecture, aligning academic integrity with instructional design. The framework offers a replicable model for fashion programs and other disciplines seeking responsible AI integration. Future research will expand empirical assessment across courses, disciplines, and institutions, examine longitudinal learning impacts, and refine discipline-specific AIAS applications to guide higher education in the AI-driven future.
D. Shen· PUPIL International Journal...· 0 citations