2026· Open Praxis· Vol 18, pp. 481-494· 0 citations· 40 references
TL;DR
The Instructional Model for Human-Centered Generative AI Engagement is introduced, a pedagogical framework designed to help faculty guide students in engaging with generative AI as a thinking partner rather than a shortcut.
Abstract
As generative AI evolves from an assistive tool to an increasingly autonomous system, higher education faces new challenges for teaching and learning. Emerging research describes the rise of the “ghost student,” who completes coursework without active participation, and the “cognitive debt” that accrues when productive struggle is outsourced to automated systems. These developments expose the limits of reactive, compliance-focused approaches. In response, this conceptual paper argues that purposeful, human-centered AI engagement offers a better path forward. Drawing on constructionist learning theory, we introduce the Instructional Model for Human-Centered Generative AI Engagement, a pedagogical framework designed to help faculty guide students in engaging with generative AI as a thinking partner rather than a shortcut. The model consists of five recursive phases: critical and ethical awareness, prompt literacy, AI-supported learning, reflection and revision, and independent application. Central to its implementation is the Prompt Literacy Cycle, nested within Phase Two, which guides students through iterative prompting, critical interpretation of LLM-generated outputs, and reflective revision. Together, these elements support a shift towards process-oriented assessment while fostering ethical awareness, metacognition, and intentional engagement with LLM-mediated knowledge construction.
Generative artificial intelligence (GenAI) is rapidly transforming pedagogical practices in higher education by generating explanations, feedback, simulations, learning resources, and dialogic prompts. Existing AI frameworks in education predominantly conceptualize AI through functional roles, such as tutoring, assessment-centric models, institutional governance principles, or learner literacy perspectives. However, higher education institutions often regard GenAI as a complementary tool while simultaneously framing it as a threat to academic integrity, triggering reactive responses such as prohibition, surveillance, and detection. These framings leave a theoretical gap, offering limited insight into how GenAI redefines pedagogical agency, responsibility, and knowledge work in everyday interactions among instructors, students, and institutional structures. To address this gap, the present study proposes a nested instructor-student-GenAI triadic conceptual model for higher education. The model is derived through a focused integrative interdisciplinary synthesis that brings together literature from higher education, educational technology, learning sciences, instructional design, human-computer interaction, cognitive psychology, policy, ethics, and institutional governance. The model positions GenAI as a bounded didactic-pedagogical mediator operating within a shared didactic mediation space. Higher education institutions are conceptualized as the governance layer that enables, constrains, and legitimizes triadic practice through policies, infrastructure, regulations, and accountability mechanisms, while wider stakeholders shape external expectations. The study further formulates researchable propositions and discipline-sensitive implications to support future empirical validation and responsible GenAI integration in higher education.
Sharmila Rani Moganadas, Freddy Marín-González, Shwu Huey Nun et al.· Frontiers in Education· 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.
It is argued that AI adoption should not be viewed as a pedagogical rupture, but as a continuation of a long-standing shift toward learner-centred, meaning-focused, and interaction-driven language education.
A. Fan· International Journal of Sec...· 0 citations
The rapid adoption of generative artificial intelligence has challenged long-standing assumptions about assessment in higher education. While much of the current conversation has focused on academic integrity and AI detection, these approaches do not address a more fundamental question: What evidence is needed to make valid judgments about student learning? This paper argues that the emergence of AI has not changed the purpose of assessment but has highlighted the need to reconsider how learning is demonstrated. Building upon authentic assessment and evidence-centered assessment, the paper introduces Human-Centered Assessment as a conceptual approach that emphasizes making student thinking visible through authentic voice, reflection, judgment, context, process transparency, and ethical AI integration. It then presents three complementary frameworks that guide faculty from understanding the principles of human-centered assessment to evaluating existing assignments and redesigning assessments for AI-mediated learning environments. Together, these frameworks shift the conversation from detecting AI to designing assessments that generate richer evidence of student learning. The paper concludes by discussing implications for assessment practice, faculty development, and future research.
Laura De La Cruz· Intersection: A Journal at t...· 0 citations
A holistic framework that integrates inquiry-based learning (IBL) with artificial intelligence (AI) to support learning is proposed, arguing that the sustainability of such a system depends on shifting assessment from content mastery to measurable complex thinking skills.
Lori B. Doyle, Jill L. Swisher· Educational Point· 0 citations
This paper presents a conceptual and design-based framework for integrating Artificial Intelligence (AI) into higher education art instruction through human–AI collaboration, focusing on prompt-based generation systems such as Adobe Firefly. The study proposes an instructional model that positions AI as a co-creative partner supporting creativity, reflection, and critical thinking in art education. While AI is increasingly used in creative fields, most existing studies focus on its technical capabilities rather than its pedagogical potential. This study was conceptual research addresses that gap by proposing a structured instructional model that positions AI as a co-creative partner in the learning process, not just a tool for production. The study was ideally suited for undergraduate students in an art and multimedia program. The instructional upon design framework comprises three interconnected components: (1) AI as a creative partner, (2)prompt as a pedagogical tool, and (3) curriculum integrationfor critical reflection. The paper illustrates how these elementscan be applied through hypothetical classroom activities, suchas prompt development workshops, mapping exercises, anditerative refinement tasks. These design scenarios demonstratehow prompt-based engagement with AI can cultivate creativeexploration and critical awareness. The proposed conceptualmodel serves as a foundation for future empirical research andcurriculum innovation, offering educators adaptable strategiesfor incorporating AI-supported creativity and ethical reflectionacross diverse learning contexts. This paper does not reportempirical data or classroom implementation. Instead, itproposes a conceptual and design-based framework intended toguide future empirical and curriculum-based studies in human-AI collaboration.
Waiyawat Saitum· International Journal of Inf...· 0 citations
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