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Adaptive user interfaces and universal usability through plasticity of user interface design

机译：通过用户界面设计的可塑性自适应用户界面和通用可用性

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A review of research on universal usability, plasticity of user interface design and facilitation of interface development with universal usability is presented. The survey was based on 165 research papers spanning over fifty-five years. The foundations of adaptive or intelligent user interfaces (AUI or IUI) are presented, three core domains being focused upon: Artificial Intelligence (AI), User Modelling (UM) and Human-Computer Interaction (HCI). For comparison of the various AUIs, a proposed taxonomy is given. One conclusion is that an efficient training vector for fast optimal convergence of the machine-learning algorithm is a necessity, but key to this is the bounding of the dataset, the goal being to achieve an accurate user preference model, which has to be built from a limited number of datasets obtained from the human interaction. More research also needs to be conducted to ascertain the usefulness and effectiveness of IUIs compared against AUIs.With the global mobility of users, interface design must take account of the abilities and cultures of users, derived from actual user behaviour and not on their feedback. A key question is whether the interface should be adaptive under system control or be made adaptable under user control. A need is identified for an "afferential component" that stores a priori information about the end user, an "inferential component" that determines to what extent the user interface actually needs to be adapted, and the "efferential component" that actually determines how the adaptivity is applied seamlessly to the system. Application to e-learning is a priority: the use of machine intelligence to achieve appropriate learnability, ideally enhanced by "Playful interaction", was found to be desirable. Universal application of adaptation lies in the future, but AUI properties cannot be ascertained while disregarding the other parameters of the system in which it will be used. A more complete understanding of the human mental model is necessary, requiring a highly multidisciplinary approach and cooperation between diverse researchers.Finally, a performance evaluation of plasticity of user interface was conducted: it is concluded that the use of dynamic techniques can enhance the user experience to a much greater extent than more basic approaches, although optimisation of usability parameter trade-offs needs further attention.It is noted that most of the work reviewed originated from a limited range of cultural perspectives. To make an interface simultaneously usable for users from a diverse range of cultural backgrounds will require a very large amount of adaptation, but the powerful principles of plasticity of user interface design hold the future promise of an optimum tool to achieve cross-cultural usability.
机译：介绍了普遍可用性研究的综述，具有普遍可用性的用户界面设计和促进界面开发的可塑性。该调查基于165篇跨越五十五年的研究论文。提出了自适应或智能用户界面（AUI或IUI）的基础，三个核心域集中在：人工智能（AI），用户建模（UM）和人机交互（HCI）。为了比较各种Auis，给出了一个拟议的分类法。一个结论是，一个有效的训练矢量用于机器学习算法的快速最佳融合是必要的，但是这是数据集的界限，目标是实现准确的用户偏好模型，必须从中构建从人类相互作用获得的有限数量的数据集。需要进行更多的研究以确定IUIS与AUIS相比的有用性和有效性。在全球用户的移动性，界面设计必须考虑来自实际用户行为的用户的能力和文化，而不是在反馈中。一个关键问题是接口是否应在系统控制下自适应，或者在用户控制下进行适应性。对于存储有关最终用户的先验信息的“交流组件”确定了一个“推理组件”，该“推理组件”确定了实际需要调整的用户界面的程度，以及实际确定如何确定的“兴奋部件”适应性可无缝应用于系统。在电子学习中的应用是优先级：使用机器智能来实现适当的可读性，理想地通过“俏皮互动”来增强，被发现是可取的。适应的普遍应用在未来，但无法确定AUI属性，同时忽略将使用它的系统的其他参数。需要更完全了解人力模型，需要高度多学科方法和不同的研究人员之间的合作。最后，进行了用户界面的可塑性评估：得出结论，动态技术的使用可以增强用户体验在更大程度上比更基本的方法更大，尽管可用性参数权衡的优化需要进一步关注。但是指出，大多数工作审查起源于有限的文化视角。要使用户同时可用于来自各种文化背景的用户，需要大量的适应，但用户界面设计的功能强大原则具有最佳工具的未来承诺，以实现跨文化可用性。

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