Digital art portfolios serve as impactful mediums for artists to convey their
visions, weaving together visuals, audio, interactions, and narratives.
However, without technical backgrounds, design students often find it
challenging to translate creative ideas into tangible codes and designs, given
the lack of tailored resources for the non-technical, academic support in art
schools, and a comprehensive guiding tool throughout the mentally demanding
process. Recognizing the role of companionship in code learning and leveraging
generative AI models' capabilities in supporting creative tasks, we present
PortfolioMentor, a coding companion chatbot for IDEs. This tool guides and
collaborates with students through proactive suggestions and responsible Q&As
for learning, inspiration, and support. In detail, the system starts with the
understanding of the task and artist's visions, follows the co-creation of
visual illustrations, audio or music suggestions and files, click-scroll
effects for interactions, and creative vision conceptualization, and finally
synthesizes these facets into a polished interactive digital portfolio.

PortfolioMentor 是一款用于 IDE 的编码伴侣聊天机器人工具，通过积极的建议和负责任的问答，引导并与学生合作，支持学习、灵感和支持，具体包括任务理解、视觉插图、音频或音乐建议和文件、交互式点击滚动效果、创意愿景概念化等，最终将这些方面综合成一个精细交互的数字作品集。

PortfolioMentor: 多模态生成 AI 助手用于学习和设计互动数字艺术作品集

PortfolioMentor: Multimodal Generative AI Companion for Learning and  Crafting Interactive Digital Art Portfolios

The availability of large idea repositories (e.g., the U.S. patent database)
could significantly accelerate innovation and discovery by providing people
with inspiration from solutions to analogous problems. However, finding useful
analogies in these large, messy, real-world repositories remains a persistent
challenge for either human or automated methods. Previous approaches include
costly hand-created databases that have high relational structure (e.g.,
predicate calculus representations) but are very sparse. Simpler
machine-learning/information-retrieval similarity metrics can scale to large,
natural-language datasets, but struggle to account for structural similarity,
which is central to analogy. In this paper we explore the viability and value
of learning simpler structural representations, specifically, "problem
schemas", which specify the purpose of a product and the mechanisms by which it
achieves that purpose. Our approach combines crowdsourcing and recurrent neural
networks to extract purpose and mechanism vector representations from product
descriptions. We demonstrate that these learned vectors allow us to find
analogies with higher precision and recall than traditional
information-retrieval methods. In an ideation experiment, analogies retrieved
by our models significantly increased people's likelihood of generating
creative ideas compared to analogies retrieved by traditional methods. Our
results suggest a promising approach to enabling computational analogy at scale
is to learn and leverage weaker structural representations.

通过学习较简单的 “问题模式” 结构表示，并结合众包和递归神经网络从产品描述中提取目的和机制向量表示，使我们在寻找类比时可以比传统信息检索方法获得更高的精确度和召回率，在创意发想实验中，模型检索的类比与传统方法相比， 显著提高了人们产生创造性想法的可能性，这一结果表明，在许多大规模的现实生活情境下，学习和利用较弱的结构表示是实现计算类比的一种很有前途的方法。