The RAISE Framework (or Responsible AI Support for Education) is a proposed structure for an institution-wide approach for managing the ethical and effective integration of generative AI in higher education. As Phase 1 of a two-tiered strategy, it addresses the current lack of coherent AI policies by establishing five key pillars: 1) Regulation, 2) AI Literacy, 3) Instructional Redesign, 4) Support Infrastructure, and 5) Evaluation. These pillars guide institutions in defining acceptable AI use, embedding AI literacy into curricula, redesigning assessments to resist misuse, providing AI-aware support services, and continually refining practices through feedback and audits. RAISE enables institutions to move from a reactive stance to a proactive, sustainable model that embeds academic integrity, ethical awareness, and inclusive support at the structural level. This foundational phase sets the stage for pedagogical innovation in Phase 2 (RAISE+), ensuring a cohesive and future-ready response to AI in education.
RAISE Framework and RAISE+ Framework Implementation Strategy
Phase 1 – RAISE Framework (Responsible AI Support for Education)
The RAISE Framework provides a systematic blueprint for institutional transformation, ensuring that higher education institutions can proactively and ethically respond to the integration of generative AI. As an illustration, Table 1 below presents an implementing strategy to institutions to integrate AI in higher education:
Table 1 Phase 1 RAISE Framework Implementation Strategy
| Component | Description | Institutional Implementation |
| R – Regulation & Policy Development | Establish clear, transparent, and adaptable institutional policies on AI usage in academic work. | – Define acceptable use (e.g., AI as brainstorming tool vs. full text generation) – Include AI usage in academic honesty policies – Require AI-use disclosure statements in submissions |
| A – AI Literacy & Training | Equip students and faculty with critical understanding and ethical awareness of AI tools. | – Develop AI literacy modules embedded in writing courses – Offer workshops for staff on evaluating AI-influenced work – Provide tutorials on prompt engineering, bias, hallucinations |
| I – Instructional Redesign | Redesign assignments and assessment formats to reduce misuse and promote higher-order thinking. | – Shift toward process-based and reflective writing tasks – Use oral defenses or AI-free in-class writing as assessment checks – Emphasize metacognitive activities (e.g., AI audit logs) |
| S – Support Infrastructure | Create access points and mentoring systems for ethical, informed use of AI in learning. | – Writing centers offer “AI-supported writing” services – Include AI prompts and critique exercises in tutorials – Create peer mentorship models for AI-augmented learning |
| E – Evaluation & Iteration | Continuously evaluate the effectiveness of AI policies and teaching practices. | – Collect feedback from students and staff each semester – Use case studies to refine practices – Monitor detection tools and update guidance accordingly |
The implementation strategy of RAISE ensures that institutions move beyond reactive measures, such as inconsistent AI bans or isolated detection efforts, and instead establish a forward-looking, ethical, and adaptive infrastructure. By embedding ethical AI use into institutional culture, policies, training, and assessment structures, RAISE shall enable academic communities to build clarity, confidence, and consistency in their engagement with LLMs.
Fundamentally, RAISE is not just a regulatory framework but it is also a capacity-building model. It aims to empower institutions to cultivate digital citizenship, foster interdisciplinary dialogue, and align technology with educational values. This lays the groundwork for Phase 2, the RAISE+ Framework, which translates these institutional commitments into pedagogically grounded practices for English academic writing, where concerns about originality, writing autonomy, and ethical authorship are most critical.
Phase 2 – RAISE+ Framework Implementation Strategy
Following the institutional foundation established by the RAISE Framework, Phase 2 introduces the RAISE+ Framework which is an enhanced, discipline-specific strategy for addressing the pedagogical challenges and opportunities associated with ChatGPT and other Large Language Models (LLMs) particularly in English academic writing instruction. English academic writing, specifically in ESL/EAP contexts, has been uniquely impacted by generative AI. While LLMs can support learners through brainstorming, language scaffolding, and idea organization, they also raise serious concerns about writing authenticity, authorship, and the negative impact of AI in critical thinking. RAISE+ responds to this by operationalizing institutional policy into classroom practice, ensuring ethical and pedagogically sound use of AI throughout the learning process.
The RAISE+ Framework implementation strategy retains the five core pillars of RAISE, but each is adapted with a writing-specific focus as illustrated in Table 2 below.
Table 2 RAISE+ Framework Implementation Strategy
| Component | Enhanced Focus for Academic Writing | Pedagogical Implementation |
| R – Academic Writing-Specific Regulation | Clarify what constitutes acceptable AI use in writing assignments. | – Establish differentiated AI use policies for brainstorming, paraphrasing, vs. full draft generation. – Include AI disclosure rubrics in assignment guidelines. – Require acknowledgment sections detailing AI involvement. |
| A – AI Literacy for Writers | Equip students with the ability to use AI critically and reflectively during the writing process. | – Integrate modules on AI prompt design, evaluating AI output, and distinguishing human vs. AI voice. – Provide exercises where students critique AI-generated texts for structure, tone, and logic. |
| I – Instructional Redesign | Restructure writing tasks to emphasize human agency, process, and revision. | – Design assignments in scaffolded phases: AI-assisted brainstorming → human outline → first draft → peer review → reflective revision. – Use in-class and oral components to validate authentic understanding. |
| S – AI-Aware Writing Support | Build support systems to help students navigate AI use ethically and productively. | – Train writing tutors to coach students on responsible AI use. – Offer annotated examples of acceptable vs. inappropriate AI assistance. – Develop “AI writing consultations” in writing centers. |
| E – Dual-Mode Evaluation | Evaluate both written output and the student’s engagement with AI. | – Use AI-reflection logs as part of grading criteria. – Include questions on AI involvement in peer review forms. – Emphasize critical reflection and revision over surface-level accuracy. |
The RAISE+ Framework enhances English academic writing instruction by promoting ethical, transparent, and effective use of AI. It encourages students to disclose and reflect on their AI use, reinforcing ethical authorship, while preserving writing autonomy through clear boundaries on acceptable use. By guiding students to critically evaluate AI-generated content, RAISE+ fosters deeper analytical skills and understanding of academic discourse. It also supports ESL/EAP learners by using AI as a scaffold for language and idea development, strengthening both confidence and independence. Essentially, RAISE+ reframes AI as a pedagogical ally, not a threat. It ensures students learn with AI rather than depend on it, supporting key competencies like argumentation and rhetorical voice through guided use. Its dual-mode evaluation focuses on both writing quality and engagement with the process, reinforcing ethical awareness. When paired with Phase 1 (RAISE), which establishes institutional policy and infrastructure, RAISE+ brings those principles into classroom practice. Jointly, they form a cohesive, vertically integrated model for responsible AI integration, enabling higher education to embrace technological innovation while preserving academic integrity and educational values. In summary, this review highlights both the opportunities and challenges of integrating ChatGPT and other LLMs into English academic writing in higher education. To move beyond reactive responses, institutions must adopt a holistic, forward-looking approach. The two-phase RAISE and RAISE+ Frameworks offer an integrated model aligning policy, pedagogy, and practice. By implementing clear policies, fostering AI literacy, redesigning assessments, and promoting ethical AI use, institutions can leverage AI’s benefits while preserving academic integrity, originality, and critical thinking at the heart of learning.
(Excerpt from Cainoy, J. P. (2026). A systematic review of ChatGPT and LLMs in English academic writing: Challenges, opportunities, and the development of the RAISE framework. In S. Khan & P. P. Pringuet (Eds.), Potential of AI to replace teachers’ expertise: Ethics and challenges. IGI Global Scientific Publishing.)