Kaihua Ding

Kaihua Ding

The proliferation of generative AI tools has rendered traditional modular assessments in computing and data-centric education increasingly ineffective, creating a disconnect between academic evaluation and authentic skill measurement. This paper presents a theoretically grounded framework for designing AI-resilient assessments, supported by formal analysis and empirical validation. We make three primary contributions. First, we establish two formal propositions. (1) Assessments composed of interconnected problems, in which outputs serve as inputs to subsequent tasks, are inherently more AI-resilient than modular assessments due to their reliance on multi-step reasoning and sustained context. (2) Semi-structured problems with deterministic success criteria provide more reliable measures of student competency than fully open-ended projects, which allow AI systems to default to familiar solution templates. These results challenge widely cited recommendations in recent institutional and policy guidance that promote open-ended assessments as inherently more robust to AI assistance. Second, we validate these propositions through empirical analysis of three university data science courses (N = 117). We observe a substantial AI inflation effect: students achieve near-perfect scores on AI-assisted modular homework, while performance drops by approximately 30 percentage points on proctored exams (Cohen d = 1.51). In contrast, interconnected projects remain strongly aligned with modular assessments (r = 0.954, p < 0.001) while maintaining AI resistance, whereas proctored exams show weaker alignment (r = 0.726, p < 0.001). Third, we translate these findings into a practical assessment design procedure that enables educators to construct evaluations that promote deeper engagement, reflect industry practice, and resist trivial AI delegation.

Kaihua Ding

Reliable evaluation of AI systems remains a fundamental challenge when ground truth labels are unavailable, particularly for systems generating natural language outputs like AI chat and agent systems. Many of these AI agents and systems focus on entity-centric tasks. In enterprise contexts, organizations deploy AI systems for entity linking, data integration, and information retrieval where verification against gold standards is often infeasible due to proprietary data constraints. Academic deployments face similar challenges when evaluating AI systems on specialized datasets with ambiguous criteria. Conventional evaluation frameworks, rooted in supervised learning paradigms, fail in such scenarios where single correct answers cannot be defined. We introduce VB-Score, a variance-bounded evaluation framework for entity-centric AI systems that operates without ground truth by jointly measuring effectiveness and robustness. Given system inputs, VB-Score enumerates plausible interpretations through constraint relaxation and Monte Carlo sampling, assigning probabilities that reflect their likelihood. It then evaluates system outputs by their expected success across interpretations, penalized by variance to assess robustness of the system. We provide formal theoretical analysis establishing key properties including range, monotonicity, and stability along with concentration bounds for Monte Carlo estimation. Through case studies on AI systems with ambiguous inputs, we demonstrate that VB-Score reveals robustness differences hidden by conventional evaluation frameworks, offering a principled measurement framework for assessing AI system reliability in label-scarce domains.

Kaihua Ding, Jingsong Cui, Mohammad Soltani et al.

The field of causal Machine Learning (ML) has made significant strides in recent years. Notable breakthroughs include methods such as meta learners (arXiv:1706.03461v6) and heterogeneous doubly robust estimators (arXiv:2004.14497) introduced in the last five years. Despite these advancements, the field still faces challenges, particularly in managing tightly coupled systems where both the causal treatment variable and a confounding covariate must serve as key decision-making indicators. This scenario is common in applications of causal ML for marketing, such as marketing segmentation and incremental marketing uplift. In this work, we present our formally proven algorithm, iterative causal segmentation, to address this issue.