At the heart of ZUFE’s approach lies a project-based learning methodology, which uses current and highly relevant societal issues to anchor the educational experience. A prime example is their module focused on applying AIGC tools to analyze climate policy—one of today’s most urgent and multifaceted global challenges. The course employs a progressive teaching loop spanning five core modules, each emphasizing a fundamental aspect of information literacy: acquisition, utilization, exchange, evaluation, and future exploration. This modular structure ensures continuity, reinforcing each step as students move from understanding AI technologies to engaging in integrative, ethical, and interdisciplinary analysis.

The initial stage immerses students directly into the technical foundations of information acquisition with cutting-edge AI models. Using open-source platforms such as DeepSeek, learners explore the inner workings of GPT models, dissecting natural language processing (NLP) workflows to appreciate how algorithm design impacts output quality. Students employ the CAST framework, which guides prompt engineering by clarifying roles, actions, standards, and targets for information retrieval within the context of climate policy. By iteratively adjusting prompts and documenting the resulting information shifts, they gain empirical insight into phenomena like AI “hallucinations”—instances where models generate plausible yet incorrect information. This foundational phase aims to develop critical awareness of AI’s capabilities and limitations, fostering metacognitive skills vital for discerning researchers.

Building on this technical foundation, the second module shifts emphasis toward information utilization and knowledge system construction. Students break down complex policy topics—such as the EU’s carbon taxation mechanisms—into discrete legal, technical, and economic components. Utilizing visualization tools like XMind, they reconstruct fragmented data into coherent, scalable knowledge maps. Such conceptual models contrast sharply with the superficial output of raw AIGC policy reports, revealing the hollowed-out logical scaffolding that emerges when relying solely on AI-generated content. This deconstruction and reconstruction process operationalizes knowledge-building, enabling students to verify, critique, and internalize information, laying the groundwork for more sophisticated academic dialog in the next module.

Information exchange, the third stage of the framework, offers a dynamic arena for students to engage in dialogical academic research through role-play and debate. By assuming the personas of diverse stakeholders—including government negotiators, renewable energy CEOs, climate activists, and AI ethics reviewers—students confront the complex interplay of science, ethics, economics, and policy within climate discourse. The simulated policy negotiations catalyze critical reasoning and perspective-taking, essential components of complex thinking. Guided by instructors functioning as cognitive scaffolds, learners identify knowledge gaps and contextual biases in AI-generated texts, collaboratively building an integrative policy analysis framework. This dialogic process not only reinforces content understanding but also hones communication skills pivotal for professional and civic engagement.

The course then pivots to information evaluation and ethical scrutiny in its fourth module. Recognizing the profound moral and security implications of AI-assisted information processing, students are tasked with rigorously labeling AI-generated content, documenting model versions, and verifying traceability. This meticulous approach cultivates transparency and accountability, allowing learners to detect biases—for example, whether AI underrepresents developing nations’ contributions to emissions reductions. Privacy concerns receive equal weight, with students recording query details to safeguard personal data amid widespread AIGC use. Embedding these ethical practices ensures that students evolve into responsible information consumers and producers, equipped to navigate the evolving regulatory and social landscape.

Crucially, the fifth module emphasizes interdisciplinary integration and innovative future exploration. Climate policy epitomizes a complex problem domain intersecting environmental economics, technology, social equity, and labor dynamics. Students leverage conceptual mapping tools like CmapTools to visualize relationships among key elements such as decarbonization technologies, employment transformation, and AI-driven economic risk assessments. By synthesizing knowledge from disparate disciplines, learners cultivate systems thinking and creativity, enabling them to approach challenges holistically rather than in isolated silos. The capstone research report consolidates this comprehensive learning experience, fostering the capacity to innovate within dynamically interconnected societal systems.

Together, these five stages craft a coherent competency chain: understanding, constructing, dialoguing, reviewing, and creating. This scaffolded methodology transcends rote learning, instead nurturing core elements of complex thinking—critical analysis, metacognition, systems analysis, problem-solving, and creative synthesis. Embedding such a pedagogical philosophy within a single thematic case study ensures depth and continuity, enabling students to internalize these skills through sustained, contextualized practice.

Beyond the classroom, the course extends its impact through a thoughtfully tiered extracurricular framework dedicated to progressively cultivating complex thinking across students’ academic trajectories. Expansion courses, embedded within second classrooms or as customized training programs, deepen skills in immersive, flexible environments. Practical workshops employ AI tools to bolster inquiry-driven literature review and scientific writing, directly enhancing scholarly competencies. Moreover, the institution organizes an undergraduate information literacy competition across China’s finance and economics universities, fostering a culture of learning through challenge and peer engagement. This multifaceted extension strategy ensures that information literacy development is continuous, cumulative, and institutionally supported.

Empirical outcomes underscore the framework’s efficacy. Pre- and post-course assessments reveal statistically significant improvements in students’ complex thinking abilities. Complementing quantitative data, student feedback highlights gains in efficient search techniques, precise analytical methodologies, and sophisticated strategies for leveraging AI in deep learning contexts. The curriculum’s impact transcends cognitive skill development; students report a paradigm shift toward integrated, critical thought processes foundational for advanced academic pursuits and professional practice. The program’s success is further evidenced externally, with course completers earning top honors in national competitions and publishing high-quality academic papers, affirming the framework’s real-world applicability and transformative potential.

This educational model offers a compelling blueprint for universities confronting the dual pressures of technological acceleration and increasing complexity in knowledge domains. By fusing technical instruction with ethical literacy, dialogic engagement, and interdisciplinary synthesis, ZUFE positions its students to thrive amid the challenges of the AI era. The framework not only equips learners with the pragmatics of managing AI tools but also instills a richer epistemological awareness and critical stance necessary to steward information responsibly and innovatively.

As AI continues to evolve, reshaping the methods by which information is generated, disseminated, and interpreted, education systems must adapt accordingly. The multidimensional design of ZUFE’s “Information Literacy and Practice” course exemplifies a forward-looking commitment to cultivating resilient, reflective, and resourceful thinkers. It transcends simplistic technical training, heralding a new paradigm in higher education where complex thinking is nurtured through experiential, dialogical, and interdisciplinary learning anchored in pressing real-world issues.

In sum, this pioneering framework not only validates the theoretical conception of a holistic information literacy education but also demonstrates its operational viability and measurable success. Its emphasis on integrating AI tool mastery with ethical evaluation and systemic reasoning exemplifies the future of information literacy education. Institutions aiming to prepare their students for the realities of an AI-saturated information ecosystem will find in this model a scalable, adaptable roadmap for fostering the complex competencies indispensable for navigating tomorrow’s intellectual and professional landscapes.

As educators and policymakers seek to harness the potentials of AI without succumbing to its pitfalls, frameworks such as this provide a vital conceptual and practical foundation. They remind us that fostering human agency—through complex thinking and critical engagement—is as essential as advancing machine capabilities. In this light, Zhejiang University of Finance and Economics’ initiative stands as a beacon of innovative pedagogy, blending philosophy, technology, and social responsibility into a transformative educational experience for the digital age.

Subject of Research:
Information literacy education framework designed to foster complex thinking skills in college students, emphasizing the integration of AI-generated content tools within higher education curricula.

Article Title:
A philosophical perspective on constructing an information literacy education framework to foster college students’ complex thinking skills.

Article References:
RUAN, Q. A philosophical perspective on constructing an information literacy education framework to foster college students’ complex thinking skills.
Humanit Soc Sci Commun 12, 1409 (2025). https://doi.org/10.1057/s41599-025-05760-5

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AI Generated

The ABC+ model brings to the forefront three critical dimensions of engagement: Where to act, when to think, and how to feel. This tripartite framework integrates behavioral engagement ("where to act"), cognitive engagement ("when to think"), and emotional engagement ("how to feel") with additional components that explore the interplay between these dimensions. By weaving these elements into a cohesive model, the researchers aim to capture the complexity of student engagement in academic settings, especially within STEM disciplines which often require high levels of sustained focus, problem-solving, and emotional resilience.

At the core of the model is the understanding that learning engagement is not a monolithic construct but a dynamic interplay between actions taken by students within learning environments, their strategic cognitive processes, and their emotional states. This comprehensive approach challenges prior models that tended to isolate either behavioral participation or cognitive effort, ignoring the nuanced emotional undertones that can either facilitate or impede learning. Through intricate analysis, McChesney and collaborators demonstrate that the orchestration of these three factors significantly predicts academic performance, suggesting that interventions that consider these interconnected variables can enhance learning outcomes.

One particularly compelling aspect of the ABC+ model is its emphasis on timing and context—understanding "when to think" as a crucial element that distinguishes superficial engagement from deep cognitive involvement. Students often engage with material superficially, performing tasks without genuine processing, leading to less effective learning. The model illuminates how strategic engagement—choosing optimal moments for reflection and problem-solving—can enhance comprehension and retention. This insight not only clarifies why some students perform better despite similar behavioral engagement but also guides instructional designs tailored to encourage mindful cognition.

The emotional component, captured by the "how to feel" dimension, addresses the powerful influence of affective states on motivation and perseverance. Traditional models often neglect how feelings of anxiety, interest, or confidence modulate engagement, yet these factors play a critical role in sustaining effort, especially in challenging STEM courses. By incorporating a robust affective component, the ABC+ model foregrounds the necessity of supportive learning environments that foster positive emotions and mitigate negative ones, thus unlocking students’ potential to perform at their best.

Furthermore, the model expands into what the authors term the "plus" aspects, which reflect contextual variables such as social dynamics, environmental factors, and individual differences. This comprehensive lens allows educators and researchers to appreciate the multifaceted nature of engagement beyond mere individual behavior, recognizing how classroom culture, peer interactions, and even broader institutional policies influence how students engage. This holistic perspective is particularly vital for addressing equity and inclusion in STEM education, ensuring that interventions consider varied learner backgrounds and needs.

Methodologically, the study employs a blend of quantitative and qualitative techniques to validate the ABC+ model. The researchers analyzed data from diverse student populations, encompassing various STEM disciplines and educational levels, to ensure the model’s generalizability. By correlating the dimensions of engagement with objective measures of academic performance—such as grades, retention rates, and standardized tests—they establish the predictive power of the model. Complementary qualitative data gleaned from student interviews and focus groups enrich the findings, offering vivid narratives that illustrate how engagement strategies manifest in real-world learning scenarios.

The implications of this research extend beyond the theoretical into the practical realm of instructional design and policy-making. Educators can leverage the ABC+ model to craft learning experiences that encourage not only active participation but also metacognitive awareness and emotional regulation. For instance, designing curricula that allocate time for reflection (addressing "when to think") and incorporating emotionally supportive feedback mechanisms (tackling "how to feel") can transform traditional lecture formats into vibrant, student-centered environments conducive to deeper learning.

Moreover, the ABC+ model facilitates the identification of students at risk of disengagement by highlighting early warning signs across behavioral, cognitive, and emotional domains. Interventions can thus be more precisely targeted, promoting timely support that addresses specific deficits rather than applying generic remedies. This tailored approach promises to reduce attrition rates in demanding STEM fields, where the balance of challenge and support is crucial for student success.

From a technological standpoint, the model suggests exciting opportunities for integrating adaptive learning technologies and artificial intelligence tools. By monitoring indicators aligned with the ABC+ dimensions—for example, tracking behavioral data on task engagement, cognitive patterns through problem-solving analytics, and emotional states via sentiment analysis—educational platforms could dynamically adjust content delivery and support structures, personalizing the learning journey to optimize engagement continuously.

The ABC+ model’s nuanced appreciation of engagement also encourages future research to investigate cross-cultural variations and the impact of socioeconomic factors on learning processes. As education becomes increasingly globalized, understanding how diverse learner populations experience engagement differently can inform more inclusive pedagogies, reducing achievement gaps and promoting wider participation in STEM careers.

Additionally, this model underscores the importance of teacher training programs incorporating engagement science, equipping educators with strategies to recognize and foster balanced engagement in their students. Professional development initiatives can integrate the ABC+ framework, enabling instructors to intentionally design lessons that harmonize behavioral, cognitive, and affective components, ultimately enhancing classroom dynamics and student outcomes.

In sum, McChesney and colleagues’ ABC+ model offers a transformative lens through which to view learning engagement, moving beyond reductive approaches and embracing the complexity inherent in human cognition and emotion. The model’s comprehensive nature invites educators, researchers, and policy-makers to reconceptualize engagement not as a static trait but as a fluid, interactive process that can be cultivated and optimized across educational contexts.

Given the model’s robust connection to academic performance, it holds promise for reshaping STEM education profoundly, providing a scaffold upon which future innovations in pedagogy, assessment, and learning technologies can be built. As institutions grapple with evolving demands and diverse learner needs, models like ABC+ serve as critical guides to fostering environments where students not only act and think but also feel and thrive.

The ABC+ model thus stands at the cutting edge of educational research, offering evidence-based pathways to unlocking human potential through engaged, thoughtful, and emotionally grounded learning. Its publication marks a significant milestone in our understanding of academic engagement, inspiring a new generation of studies and applications aimed at transforming education for the demands of the 21st century.

Subject of Research: Learning engagement and its relationship to academic performance within STEM education.

Article Title: Where to act, when to think, and how to feel: The ABC + model of learning engagement and its relationship to the components of academic performance.

Article References:
McChesney, E.T., Schunn, C.D., DeAngelo, L. et al. Where to act, when to think, and how to feel: The ABC + model of learning engagement and its relationship to the components of academic performance. IJ STEM Ed 12, 31 (2025). https://doi.org/10.1186/s40594-025-00555-1

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