AI 트랜스포메이션이 선교 패러다임에 미친 영향을 분석하고, 지속가능한 선교 관점에서 복음 텍스트와 디지털 콘텍스트의 균형을 모색한다. 2016년 이후 한국 선교의 정체 현상은 ‘질적 선교’로의 전환을 요청했으며, 4차 산업혁명 기술은 선교 방법과 구조의 근본적인 재편을 가속화하고 있다. 한국세계선교협의회(KWMA)의 디지털 선교 로드맵과 실제 교회 시스템 구축 사례를 토대로, 폴 히버트의 비평적 상황화 이론에 기초한 ‘디지털 통합 모델’을 제안한다. 이 모델은 네트워 크, 플랫폼, 하이브리드, AI 보조라는 네 가지 핵심 차원을 통해 선교 현장의 효율성을 극대화하고 사역의 질적 향상을 도모한다. 특히, AI 시대의 기술적 편향과 윤리적 문제에 대한 신학적 성찰을 강조하며, 디지털 기술이 성령의 사역을 대체하는 것이 아니라 보조하는 도구임을 명확히 한다. 결론적으로, 데이터 기반의 공유 플랫폼을 통해 선교 역량을 강화하고 선교적 유산을 축적함으로써, 복음의 본질을 수호하는 동시에 급변하는 디지털 환경 속에서 다음 세대를 향한 지속가능한 선교 방향을 제시하고자 한다.

This study presents a career-integrated English education model incorporating generative AI through two tracks: (1) content development, where English education majors in four career-oriented groups (teaching vs. non-teaching; decided vs. undecided) created TOEIC-format listening materials aligned with their interests; and (2) classroom application, where the AI-generated content was implemented in first-year general classes with 103 students. Career maturity was measured by a 30-item scale, and content effectiveness by ratings of difficulty, appropriateness, usefulness, and effectiveness. Results showed that content-related factors, not learner background, best predicted overall satisfaction. Although no statistically significant differences were found, education majors tended to rate the materials as slightly more useful and slightly less difficult. Pre–post tests showed improved decisiveness, suggesting that AI-integrated, career-oriented instruction enhanced students’ confidence in career decision-making and demonstrated the potential of generative AI in fostering professional competence and career readiness.

This study proposes and verifies a next-generation 3D modeling pipeline integrating Generative AI and Photogrammetry techniques. Two types of objects were selected for experimentation: a bust combining characteristics of both objects and characters, and a stone slab exhibiting planar and symmetrical features. Firstly, videos depicting these objects from various viewpoints were generated using generative AI models. Secondly, these videos were processed through Photogrammetry software to produce high-quality 3D meshes and diffuse textures. Lastly, post-processing including high and Low-polygon mesh separation, UV unwrapping, and texture baking was performed, successfully producing Physically Based Rendering (PBR)-compatible game assets. The proposed pipeline demonstrates significant potential for practical applications across various industries, and further advances in generative AI and future research are expected to broaden this novel approach to digital asset creation.